Tom Silver
@tomssilver.bsky.social
Assistant Professor @Princeton. Developing robots that plan and learn to help people. Prev: @Cornell, @MIT, @Harvard.
https://tomsilver.github.io/
https://tomsilver.github.io/
This week's #PaperILike is "Lifelong Robot Library Learning: Bootstrapping Composable and Generalizable Skills for Embodied Control with Language Models" (Tziafas & Kasaei, ICRA 2024).
DreamCoder-like robot skill learning. Refactoring helps!
PDF: arxiv.org/abs/2406.18746
DreamCoder-like robot skill learning. Refactoring helps!
PDF: arxiv.org/abs/2406.18746
Lifelong Robot Library Learning: Bootstrapping Composable and Generalizable Skills for Embodied Control with Language Models
Large Language Models (LLMs) have emerged as a new paradigm for embodied reasoning and control, most recently by generating robot policy code that utilizes a custom library of vision and control primi...
arxiv.org
November 9, 2025 at 1:52 PM
This week's #PaperILike is "Lifelong Robot Library Learning: Bootstrapping Composable and Generalizable Skills for Embodied Control with Language Models" (Tziafas & Kasaei, ICRA 2024).
DreamCoder-like robot skill learning. Refactoring helps!
PDF: arxiv.org/abs/2406.18746
DreamCoder-like robot skill learning. Refactoring helps!
PDF: arxiv.org/abs/2406.18746
Happy to share some of the first work from my new lab! This project has shaped my thinking about how we can effectively combine planning and RL. Key idea: start with a planner that is slow and "robotic", then use RL to discover shortcuts that are fast and dynamic. (1/2)
Robots can plan, but rarely improvise. How do we move beyond pick-and-place to multi-object, improvisational manipulation without giving up completeness guarantees?
(1/8)
(1/8)
November 5, 2025 at 3:22 PM
Happy to share some of the first work from my new lab! This project has shaped my thinking about how we can effectively combine planning and RL. Key idea: start with a planner that is slow and "robotic", then use RL to discover shortcuts that are fast and dynamic. (1/2)
This week's #PaperILike is "Monte Carlo Tree Search with Spectral Expansion for Planning with Dynamical Systems" (Riviere et al., Science Robotics 2024).
A creative synthesis of control theory and search. I like using the Gramian to branch.
PDF: arxiv.org/abs/2412.11270
A creative synthesis of control theory and search. I like using the Gramian to branch.
PDF: arxiv.org/abs/2412.11270
Monte Carlo Tree Search with Spectral Expansion for Planning with Dynamical Systems
The ability of a robot to plan complex behaviors with real-time computation, rather than adhering to predesigned or offline-learned routines, alleviates the need for specialized algorithms or training...
arxiv.org
November 2, 2025 at 1:27 PM
This week's #PaperILike is "Monte Carlo Tree Search with Spectral Expansion for Planning with Dynamical Systems" (Riviere et al., Science Robotics 2024).
A creative synthesis of control theory and search. I like using the Gramian to branch.
PDF: arxiv.org/abs/2412.11270
A creative synthesis of control theory and search. I like using the Gramian to branch.
PDF: arxiv.org/abs/2412.11270
This week's #PaperILike is "Reality Promises: Virtual-Physical Decoupling Illusions in Mixed Reality via Invisible Mobile Robots" (Kari & Abtahi, UIST 2025).
This is some Tony Stark level stuff! XR + robots = future.
Website: mkari.de/reality-prom...
PDF: mkari.de/reality-prom...
This is some Tony Stark level stuff! XR + robots = future.
Website: mkari.de/reality-prom...
PDF: mkari.de/reality-prom...
Reality Promises
mkari.de
October 26, 2025 at 3:02 PM
This week's #PaperILike is "Reality Promises: Virtual-Physical Decoupling Illusions in Mixed Reality via Invisible Mobile Robots" (Kari & Abtahi, UIST 2025).
This is some Tony Stark level stuff! XR + robots = future.
Website: mkari.de/reality-prom...
PDF: mkari.de/reality-prom...
This is some Tony Stark level stuff! XR + robots = future.
Website: mkari.de/reality-prom...
PDF: mkari.de/reality-prom...
This week's #PaperILike is "Learning to Guide Task and Motion Planning Using Score-Space Representation" (Kim et al., IJRR 2019).
This is one of those papers that I return to over the years and appreciate more every time. Chock full of ideas.
PDF: arxiv.org/abs/1807.09962
This is one of those papers that I return to over the years and appreciate more every time. Chock full of ideas.
PDF: arxiv.org/abs/1807.09962
Learning to guide task and motion planning using score-space representation
In this paper, we propose a learning algorithm that speeds up the search in task and motion planning problems. Our algorithm proposes solutions to three different challenges that arise in learning to ...
arxiv.org
October 19, 2025 at 3:02 PM
This week's #PaperILike is "Learning to Guide Task and Motion Planning Using Score-Space Representation" (Kim et al., IJRR 2019).
This is one of those papers that I return to over the years and appreciate more every time. Chock full of ideas.
PDF: arxiv.org/abs/1807.09962
This is one of those papers that I return to over the years and appreciate more every time. Chock full of ideas.
PDF: arxiv.org/abs/1807.09962
This week's #PaperILike is "On the Utility of Koopman Operator Theory in Learning Dexterous Manipulation Skills" (Han et al., CoRL 2023).
This and others have convinced me that I need to learn Koopman! Another perspective on abstraction learning.
PDF: arxiv.org/abs/2303.13446
This and others have convinced me that I need to learn Koopman! Another perspective on abstraction learning.
PDF: arxiv.org/abs/2303.13446
On the Utility of Koopman Operator Theory in Learning Dexterous Manipulation Skills
Despite impressive dexterous manipulation capabilities enabled by learning-based approaches, we are yet to witness widespread adoption beyond well-resourced laboratories. This is likely due to practic...
arxiv.org
October 12, 2025 at 12:38 PM
This week's #PaperILike is "On the Utility of Koopman Operator Theory in Learning Dexterous Manipulation Skills" (Han et al., CoRL 2023).
This and others have convinced me that I need to learn Koopman! Another perspective on abstraction learning.
PDF: arxiv.org/abs/2303.13446
This and others have convinced me that I need to learn Koopman! Another perspective on abstraction learning.
PDF: arxiv.org/abs/2303.13446
This week's #PaperILike is "Predictive Representations of State" (Littman et al., 2001).
A lesser known classic that is overdue for a revival. Fans of POMDPs will enjoy.
PDF: web.eecs.umich.edu/~baveja/Pape...
A lesser known classic that is overdue for a revival. Fans of POMDPs will enjoy.
PDF: web.eecs.umich.edu/~baveja/Pape...
web.eecs.umich.edu
October 5, 2025 at 2:45 PM
This week's #PaperILike is "Predictive Representations of State" (Littman et al., 2001).
A lesser known classic that is overdue for a revival. Fans of POMDPs will enjoy.
PDF: web.eecs.umich.edu/~baveja/Pape...
A lesser known classic that is overdue for a revival. Fans of POMDPs will enjoy.
PDF: web.eecs.umich.edu/~baveja/Pape...
This week's #PaperILike is "Influence-Augmented Local Simulators: A Scalable Solution for Fast Deep RL in Large Networked Systems" (Suau et al., ICML 2022).
Nice work on using fast local simulators to plan & learn in large partially observed worlds.
PDF: arxiv.org/abs/2202.01534
Nice work on using fast local simulators to plan & learn in large partially observed worlds.
PDF: arxiv.org/abs/2202.01534
Influence-Augmented Local Simulators: A Scalable Solution for Fast Deep RL in Large Networked Systems
Learning effective policies for real-world problems is still an open challenge for the field of reinforcement learning (RL). The main limitation being the amount of data needed and the pace at which t...
arxiv.org
September 28, 2025 at 12:18 PM
This week's #PaperILike is "Influence-Augmented Local Simulators: A Scalable Solution for Fast Deep RL in Large Networked Systems" (Suau et al., ICML 2022).
Nice work on using fast local simulators to plan & learn in large partially observed worlds.
PDF: arxiv.org/abs/2202.01534
Nice work on using fast local simulators to plan & learn in large partially observed worlds.
PDF: arxiv.org/abs/2202.01534
This week's #PaperILike is "Optimal Interactive Learning on the Job via Facility Location Planning" (Vats et al., RSS 2025).
I always enjoy a surprising connection between one problem (COIL) and another (UFL). And I always like work by Shivam Vats!
PDF: arxiv.org/abs/2505.00490
I always enjoy a surprising connection between one problem (COIL) and another (UFL). And I always like work by Shivam Vats!
PDF: arxiv.org/abs/2505.00490
Optimal Interactive Learning on the Job via Facility Location Planning
Collaborative robots must continually adapt to novel tasks and user preferences without overburdening the user. While prior interactive robot learning methods aim to reduce human effort, they are typi...
arxiv.org
September 21, 2025 at 3:44 PM
This week's #PaperILike is "Optimal Interactive Learning on the Job via Facility Location Planning" (Vats et al., RSS 2025).
I always enjoy a surprising connection between one problem (COIL) and another (UFL). And I always like work by Shivam Vats!
PDF: arxiv.org/abs/2505.00490
I always enjoy a surprising connection between one problem (COIL) and another (UFL). And I always like work by Shivam Vats!
PDF: arxiv.org/abs/2505.00490
This week's #PaperILike is "Hi Robot: Open-Ended Instruction Following with Hierarchical Vision-Language-Action Models" (Shi et al., ICML 2025).
I'm often asked: how might we combine ideas from hierarchical planning and VLAs? This is a good start!
PDF: arxiv.org/abs/2502.19417
I'm often asked: how might we combine ideas from hierarchical planning and VLAs? This is a good start!
PDF: arxiv.org/abs/2502.19417
Hi Robot: Open-Ended Instruction Following with Hierarchical Vision-Language-Action Models
Generalist robots that can perform a range of different tasks in open-world settings must be able to not only reason about the steps needed to accomplish their goals, but also process complex instruct...
arxiv.org
September 14, 2025 at 3:37 PM
This week's #PaperILike is "Hi Robot: Open-Ended Instruction Following with Hierarchical Vision-Language-Action Models" (Shi et al., ICML 2025).
I'm often asked: how might we combine ideas from hierarchical planning and VLAs? This is a good start!
PDF: arxiv.org/abs/2502.19417
I'm often asked: how might we combine ideas from hierarchical planning and VLAs? This is a good start!
PDF: arxiv.org/abs/2502.19417
This week's #PaperILike is "Labeled RTDP: Improving the Convergence of Real-Time Dynamic Programming" (Bonet & Geffner, 2003).
A very clear introduction to and improvement of RTDP, an online MDP planner that we should all have in our toolkits.
PDF: ftp.cs.ucla.edu/pub/stat_ser...
A very clear introduction to and improvement of RTDP, an online MDP planner that we should all have in our toolkits.
PDF: ftp.cs.ucla.edu/pub/stat_ser...
ftp.cs.ucla.edu
September 7, 2025 at 2:07 PM
This week's #PaperILike is "Labeled RTDP: Improving the Convergence of Real-Time Dynamic Programming" (Bonet & Geffner, 2003).
A very clear introduction to and improvement of RTDP, an online MDP planner that we should all have in our toolkits.
PDF: ftp.cs.ucla.edu/pub/stat_ser...
A very clear introduction to and improvement of RTDP, an online MDP planner that we should all have in our toolkits.
PDF: ftp.cs.ucla.edu/pub/stat_ser...
This week's #PaperILike is "Learning and Executing Generalized Robot Plans" (Fikes et al., 1972).
Classic early work on learning & planning from the team behind STRIPS, A* search, and Shakey the robot (www.youtube.com/watch?v=GmU7...).
PDF: stacks.stanford.edu/file/druid:c...
Classic early work on learning & planning from the team behind STRIPS, A* search, and Shakey the robot (www.youtube.com/watch?v=GmU7...).
PDF: stacks.stanford.edu/file/druid:c...
Shakey: Experiments in Robot Planning and Learning (1972)
YouTube video by Stanford University Libraries
www.youtube.com
August 31, 2025 at 2:51 PM
This week's #PaperILike is "Learning and Executing Generalized Robot Plans" (Fikes et al., 1972).
Classic early work on learning & planning from the team behind STRIPS, A* search, and Shakey the robot (www.youtube.com/watch?v=GmU7...).
PDF: stacks.stanford.edu/file/druid:c...
Classic early work on learning & planning from the team behind STRIPS, A* search, and Shakey the robot (www.youtube.com/watch?v=GmU7...).
PDF: stacks.stanford.edu/file/druid:c...
This week's #PaperILike is "Full-Order Sampling-Based MPC for Torque-Level Locomotion Control via Diffusion-Style Annealing" (Xue et al., 2024).
My favorite part is the clear running example in 2D (Fig 2 & 4). I want examples like this in my papers!
PDF: arxiv.org/abs/2409.15610
My favorite part is the clear running example in 2D (Fig 2 & 4). I want examples like this in my papers!
PDF: arxiv.org/abs/2409.15610
Full-Order Sampling-Based MPC for Torque-Level Locomotion Control via Diffusion-Style Annealing
Due to high dimensionality and non-convexity, real-time optimal control using full-order dynamics models for legged robots is challenging. Therefore, Nonlinear Model Predictive Control (NMPC) approach...
arxiv.org
August 24, 2025 at 3:16 PM
This week's #PaperILike is "Full-Order Sampling-Based MPC for Torque-Level Locomotion Control via Diffusion-Style Annealing" (Xue et al., 2024).
My favorite part is the clear running example in 2D (Fig 2 & 4). I want examples like this in my papers!
PDF: arxiv.org/abs/2409.15610
My favorite part is the clear running example in 2D (Fig 2 & 4). I want examples like this in my papers!
PDF: arxiv.org/abs/2409.15610
This week's #PaperILike is "Abstraction Refinement-guided Program Synthesis for Robot Learning from Demonstrations" (Cui et al., 2025).
And other recent papers by the same group---exciting progress in programmatic RL with applications to robotics.
PDF: herowanzhu.github.io/roboscribe.pdf
And other recent papers by the same group---exciting progress in programmatic RL with applications to robotics.
PDF: herowanzhu.github.io/roboscribe.pdf
herowanzhu.github.io
August 17, 2025 at 4:46 PM
This week's #PaperILike is "Abstraction Refinement-guided Program Synthesis for Robot Learning from Demonstrations" (Cui et al., 2025).
And other recent papers by the same group---exciting progress in programmatic RL with applications to robotics.
PDF: herowanzhu.github.io/roboscribe.pdf
And other recent papers by the same group---exciting progress in programmatic RL with applications to robotics.
PDF: herowanzhu.github.io/roboscribe.pdf
This week's #PaperILike is "Learning Value Functions with Relational State Representations for Guiding Task-and-Motion Planning" (Kim & Shimanuki, CoRL 2019).
I especially like the focus on *representations* for supporting learning and planning.
PDF: proceedings.mlr.press/v100/kim20a/...
I especially like the focus on *representations* for supporting learning and planning.
PDF: proceedings.mlr.press/v100/kim20a/...
proceedings.mlr.press
August 10, 2025 at 4:55 PM
This week's #PaperILike is "Learning Value Functions with Relational State Representations for Guiding Task-and-Motion Planning" (Kim & Shimanuki, CoRL 2019).
I especially like the focus on *representations* for supporting learning and planning.
PDF: proceedings.mlr.press/v100/kim20a/...
I especially like the focus on *representations* for supporting learning and planning.
PDF: proceedings.mlr.press/v100/kim20a/...
This week's #PaperILike is "The Utility of Temporal Abstraction in Reinforcement Learning" (Jong et al., AAMAS 2008).
My favorite underrated paper in hierarchical RL. Unpacks how options can help *or hurt* learning performance. Fun writing.
PDF: www.ifaamas.org/Proceedings/...
My favorite underrated paper in hierarchical RL. Unpacks how options can help *or hurt* learning performance. Fun writing.
PDF: www.ifaamas.org/Proceedings/...
www.ifaamas.org
August 3, 2025 at 12:30 PM
This week's #PaperILike is "The Utility of Temporal Abstraction in Reinforcement Learning" (Jong et al., AAMAS 2008).
My favorite underrated paper in hierarchical RL. Unpacks how options can help *or hurt* learning performance. Fun writing.
PDF: www.ifaamas.org/Proceedings/...
My favorite underrated paper in hierarchical RL. Unpacks how options can help *or hurt* learning performance. Fun writing.
PDF: www.ifaamas.org/Proceedings/...
This week's #PaperILike is "Width and Serialization of Classical Planning Problems" (Lipovetzky & Geffner, ECAI 2012).
If you only read a few classical planning papers, this should be one! Illuminating and practically useful.
PDF: www-i6.informatik.rwth-aachen.de/~hector.geff...
If you only read a few classical planning papers, this should be one! Illuminating and practically useful.
PDF: www-i6.informatik.rwth-aachen.de/~hector.geff...
www-i6.informatik.rwth-aachen.de
July 27, 2025 at 1:02 PM
This week's #PaperILike is "Width and Serialization of Classical Planning Problems" (Lipovetzky & Geffner, ECAI 2012).
If you only read a few classical planning papers, this should be one! Illuminating and practically useful.
PDF: www-i6.informatik.rwth-aachen.de/~hector.geff...
If you only read a few classical planning papers, this should be one! Illuminating and practically useful.
PDF: www-i6.informatik.rwth-aachen.de/~hector.geff...
This week's #PaperILike is "Stop! Planner Time: Metareasoning for Probabilistic Planning Using Learned Performance Profiles" (Budd et al., AAAI 2024).
Metareasoning is increasingly important as we continue to make progress on "reasoning."
PDF: ojs.aaai.org/index.php/AA...
Metareasoning is increasingly important as we continue to make progress on "reasoning."
PDF: ojs.aaai.org/index.php/AA...
Stop! Planner Time: Metareasoning for Probabilistic Planning Using Learned Performance Profiles
| Proceedings of the AAAI Conference on Artificial Intelligence
ojs.aaai.org
July 20, 2025 at 1:44 PM
This week's #PaperILike is "Stop! Planner Time: Metareasoning for Probabilistic Planning Using Learned Performance Profiles" (Budd et al., AAAI 2024).
Metareasoning is increasingly important as we continue to make progress on "reasoning."
PDF: ojs.aaai.org/index.php/AA...
Metareasoning is increasingly important as we continue to make progress on "reasoning."
PDF: ojs.aaai.org/index.php/AA...
This week's #PaperILike is "PushWorld: A benchmark for manipulation planning with tools and movable obstacles" (Kansky et al., 2023).
Fans of benchmarks like ARC will enjoy the simple mechanics and the difficult reasoning required.
PDF: arxiv.org/abs/2301.10289
Fans of benchmarks like ARC will enjoy the simple mechanics and the difficult reasoning required.
PDF: arxiv.org/abs/2301.10289
PushWorld: A benchmark for manipulation planning with tools and movable obstacles
While recent advances in artificial intelligence have achieved human-level performance in environments like Starcraft and Go, many physical reasoning tasks remain challenging for modern algorithms. To...
arxiv.org
July 13, 2025 at 12:10 PM
This week's #PaperILike is "PushWorld: A benchmark for manipulation planning with tools and movable obstacles" (Kansky et al., 2023).
Fans of benchmarks like ARC will enjoy the simple mechanics and the difficult reasoning required.
PDF: arxiv.org/abs/2301.10289
Fans of benchmarks like ARC will enjoy the simple mechanics and the difficult reasoning required.
PDF: arxiv.org/abs/2301.10289
This week's #PaperILike is "Effort Level Search in Infinite Completion Trees with Application to Task-and-Motion Planning" (Toussaint et al., ICRA 2024).
Addresses the meta-reasoning challenge that is core to TAMP. Toussaint is always worth a read.
PDF: www.user.tu-berlin.de/mtoussai/24-...
Addresses the meta-reasoning challenge that is core to TAMP. Toussaint is always worth a read.
PDF: www.user.tu-berlin.de/mtoussai/24-...
www.user.tu-berlin.de
July 6, 2025 at 2:19 PM
This week's #PaperILike is "Effort Level Search in Infinite Completion Trees with Application to Task-and-Motion Planning" (Toussaint et al., ICRA 2024).
Addresses the meta-reasoning challenge that is core to TAMP. Toussaint is always worth a read.
PDF: www.user.tu-berlin.de/mtoussai/24-...
Addresses the meta-reasoning challenge that is core to TAMP. Toussaint is always worth a read.
PDF: www.user.tu-berlin.de/mtoussai/24-...
This week's #PaperILike is "The Power of Resets in Online Reinforcement Learning" (Mhammedi et al., 2024).
If you're doing RL in sim, why not use the sim to its full potential? Reset to any state! (gym.Env.reset() is not all we need.)
PDF: arxiv.org/abs/2404.15417
If you're doing RL in sim, why not use the sim to its full potential? Reset to any state! (gym.Env.reset() is not all we need.)
PDF: arxiv.org/abs/2404.15417
The Power of Resets in Online Reinforcement Learning
Simulators are a pervasive tool in reinforcement learning, but most existing algorithms cannot efficiently exploit simulator access -- particularly in high-dimensional domains that require general fun...
arxiv.org
June 29, 2025 at 1:08 PM
This week's #PaperILike is "The Power of Resets in Online Reinforcement Learning" (Mhammedi et al., 2024).
If you're doing RL in sim, why not use the sim to its full potential? Reset to any state! (gym.Env.reset() is not all we need.)
PDF: arxiv.org/abs/2404.15417
If you're doing RL in sim, why not use the sim to its full potential? Reset to any state! (gym.Env.reset() is not all we need.)
PDF: arxiv.org/abs/2404.15417
This week's #PaperILike is "Learning over Subgoals for Efficient Navigation of Structured, Unknown Environments" (Stein et al., CoRL 2018).
A highly original combination of learning + planning that is still underrated (despite winning a CoRL award!)
PDF: proceedings.mlr.press/v87/stein18a...
A highly original combination of learning + planning that is still underrated (despite winning a CoRL award!)
PDF: proceedings.mlr.press/v87/stein18a...
proceedings.mlr.press
June 22, 2025 at 2:30 PM
This week's #PaperILike is "Learning over Subgoals for Efficient Navigation of Structured, Unknown Environments" (Stein et al., CoRL 2018).
A highly original combination of learning + planning that is still underrated (despite winning a CoRL award!)
PDF: proceedings.mlr.press/v87/stein18a...
A highly original combination of learning + planning that is still underrated (despite winning a CoRL award!)
PDF: proceedings.mlr.press/v87/stein18a...
This week's #PaperILike is "Long-Horizon Multi-Robot Rearrangement Planning for Construction Assembly" (Hartmann et al., TRO 2022).
Take two minutes to watch this video: www.youtube.com/watch?v=Gqho...
I don't use a lot of emojis, but 🤯
PDF: arxiv.org/abs/2106.02489
Take two minutes to watch this video: www.youtube.com/watch?v=Gqho...
I don't use a lot of emojis, but 🤯
PDF: arxiv.org/abs/2106.02489
Long-Horizon Multi-Robot Rearrangement Planning for Construction Assembly
YouTube video by Valentin Hartmann
www.youtube.com
June 15, 2025 at 12:50 PM
This week's #PaperILike is "Long-Horizon Multi-Robot Rearrangement Planning for Construction Assembly" (Hartmann et al., TRO 2022).
Take two minutes to watch this video: www.youtube.com/watch?v=Gqho...
I don't use a lot of emojis, but 🤯
PDF: arxiv.org/abs/2106.02489
Take two minutes to watch this video: www.youtube.com/watch?v=Gqho...
I don't use a lot of emojis, but 🤯
PDF: arxiv.org/abs/2106.02489
This week's #PaperILike is "From Real World to Logic and Back: Learning Generalizable Relational Concepts For Long Horizon Robot Planning" (Shah et al., 2024).
A fresh & clever approach with very impressive few-shot generalization results.
PDF: arxiv.org/abs/2402.11871
A fresh & clever approach with very impressive few-shot generalization results.
PDF: arxiv.org/abs/2402.11871
From Real World to Logic and Back: Learning Generalizable Relational Concepts For Long Horizon Robot Planning
Humans efficiently generalize from limited demonstrations, but robots still struggle to transfer learned knowledge to complex, unseen tasks with longer horizons and increased complexity. We propose th...
arxiv.org
June 8, 2025 at 12:22 PM
This week's #PaperILike is "From Real World to Logic and Back: Learning Generalizable Relational Concepts For Long Horizon Robot Planning" (Shah et al., 2024).
A fresh & clever approach with very impressive few-shot generalization results.
PDF: arxiv.org/abs/2402.11871
A fresh & clever approach with very impressive few-shot generalization results.
PDF: arxiv.org/abs/2402.11871
This week's #PaperILike is "Guaranteed Discovery of Control-Endogenous Latent States with Multi-Step Inverse Models" (Lamb et al., 2022).
Part of an exciting line of work: sites.google.com/view/agent-i...
This one has an awesome related work section.
PDF: arxiv.org/abs/2207.08229
Part of an exciting line of work: sites.google.com/view/agent-i...
This one has an awesome related work section.
PDF: arxiv.org/abs/2207.08229
June 1, 2025 at 11:51 AM
This week's #PaperILike is "Guaranteed Discovery of Control-Endogenous Latent States with Multi-Step Inverse Models" (Lamb et al., 2022).
Part of an exciting line of work: sites.google.com/view/agent-i...
This one has an awesome related work section.
PDF: arxiv.org/abs/2207.08229
Part of an exciting line of work: sites.google.com/view/agent-i...
This one has an awesome related work section.
PDF: arxiv.org/abs/2207.08229