🤖 Robots can grasp — but they can't keep a stable hold once dynamic forces (impact, torque, resistance) show up during tool-use 🛠️🪚

Our idea 💡: Start from a human-guided grasp optimization, then let RL adapt the fingers online 🔁 continuously correcting slip and rotation throughout the manipulation ✊

Results

We tested our approach on five real-world tool-use tasks. We compare against 2 baselines in the real-world: RL Base which is an RL policy trained from scratch and G2A w/o adaptation which is without the residual RL module.

Real-world Results

Click any task below to see it in action!

Grasp Robustness Under Perturbation

Our approach produces grasps that are extremely robust to random perturbations during task execution, maintaining stability even under unexpected disturbances.

Technical Summary Video

Our Team


If you have any questions, please contact Harsh Gupta.