7 papers
Remember what you did?: Learning Behavioral Memories for Partially Observable Object Manipulation
Kuancheng Wang, Seungho Yeom, Jinglin Cao +3
Long horizon, contact-rich manipulation is inherently partially observable. This is as a single visual observation rarely captures a robot's full action context, including prior at…
Safe Stochastic Explorer: Enabling Safe Goal Driven Exploration in Stochastic Environments and Safe Interaction with Unknown Objects
Nikhil Uday Shinde, Dylan Hirsch, Michael C. Yip +1
Autonomous robots operating in unstructured, safety-critical environments, from planetary exploration to warehouses and homes, must learn to safely navigate and interact with their…
Learning to Nudge: A Scalable Barrier Function Framework for Safe Robot Interaction in Dense Clutter
Haixin Jin, Nikhil Uday Shinde, Soofiyan Atar +5
Robots operating in everyday environments must navigate and manipulate within densely cluttered spaces, where physical contact with surrounding objects is unavoidable. Traditional…
Dual-Objective Reinforcement Learning with Novel Hamilton-Jacobi-Bellman Formulations
William Sharpless, Dylan Hirsch, Sander Tonkens +2
Hard constraints in reinforcement learning (RL) often degrade policy performance. Lagrangian methods offer a way to blend objectives with constraints, but require intricate reward…
From Space to Time: Enabling Adaptive Safety with Learned Value Functions via Disturbance Recasting
Sander Tonkens, Nikhil Uday Shinde, Azra BegzadiÄ +3
The widespread deployment of autonomous systems in safety-critical environments such as urban air mobility hinges on ensuring reliable, performant, and safe operation under varying…
Koopman-Hopf Hamilton-Jacobi Reachability and Control
Will Sharpless, Nikhil Shinde, Matthew Kim +2
The Hopf formula for Hamilton-Jacobi reachability (HJR) analysis has been proposed to solve high-dimensional differential games, producing the set of initial states and correspondi…