Future of Teleoperation & Prosthetics

The fusion of sEMG-based control + tendon-driven anthropomorphic robots opens doors to ultra-intuitive robotic hands for surgery, VR/AR, assistive tech, & space applications. This is the future! 🚀 #Robotics #NeuroTech

Seamless Mapping from Humans to Robots

By leveraging sEMG signals + tendon-based robotic actuation, we can create 1:1 mappings between human muscle activations and robotic movements, leading to naturalistic & highly responsive teleoperation. #MyoSuite

sEMG: The Key to Intuitive Control

Surface electromyography (sEMG) sensors, like Meta CTRL-Labs' wristband, capture muscle signals directly from the forearm, providing real-time, non-invasive control of robotic hands—eliminating occlusion issues in vision-based tracking. #emg2pose
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Why Tendon-Driven Hands?

Unlike rigid joint-actuated hands, tendon-driven robotic hands mimic human biomechanics, allowing for greater flexibility, force distribution, & adaptability in real-world tasks. Perfect for teleoperation and prosthetics! 🤖🖐️

The Human Hand: A Benchmark for Robotics

The human hand is the gold standard for dexterity & compliance. Its tendon-driven actuation enables natural, adaptive, and precise control something traditional robotic hands struggle to replicate. #MyoSuite #Robotics myosuite.readthedocs.io/en/latest/

I'm personally interested in how insights from the muscular system can inspire energy-efficient control for electric motors. This is especially relevant for today's humanoid robots, where battery life & control efficiency are critical. #ReinforcementLearning #Robotics

Why study muscle-based control in the first place? 🤔

Human muscles are energy-efficient & optimized for control precision. By learning from nature, we can design better, more efficient control policies for modern robots (e.g., humanoids, prosthetics).

In my experiments, I tested 8 RL methods (e.g., PPO, SAC, TD3) to train policies for accurate finger pose estimation. 🖐️

🎯 The goal: Move the finger into target poses using efficient muscle activations.

Spoiler: SAC performed best, closely followed by PPO!

Here’s a simple sketch showing the anatomy:
👉 Finger joints: MCP, PIP, DIP
👉 Tendons: flexion, abduction, extension

Each muscle-tendon unit works in opposition (like real muscles), providing a rich control challenge.

This intuitive design is based on Xu et al. (2012) [https://ieeexplore.ieee.org/document/6290710] and includes both biomechanical & robotic variants.