3 papers
cs.RO2026
Reinforcement Learning from Real-Robot Interaction without High-Quality State Estimates
Ryan Gao, Ashvin Anilkumar, Nicholas Corrado +2
A longstanding goal for robot reinforcement learning (RL) is that robots learn from their own physical experience, as humans and animals do. A key obstacle to learning directly fro…
cs.RO2026
Ludi: An Agentic System for Socially Intelligent Robots
Wooseong Chung, William Cong, Jakub Dworakowski +14
Robot foundation models have substantially advanced perception and control, but natural human-robot collaboration requires more than executing isolated commands. A robot must recog…
cs.RO2024
Reinforcement Learning Within the Classical Robotics Stack: A Case Study in Robot Soccer
Adam Labiosa, Zhihan Wang, Siddhant Agarwal +10
Robot decision-making in partially observable, real-time, dynamic, and multi-agent environments remains a difficult and unsolved challenge. Model-free reinforcement learning (RL) i…