arXiv Machine Learning
Diversity-Aware Experience Replay via Statistical Leverage Scores
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arXiv Machine Learning
Oct 15
Efficient Test-Time Adaptation For Robot Policies
arXiv:2610.20318v1 Announce Type: cross Abstract: Reinforcement-learning policies for robot locomotion can fail at deployment when operating conditions shift (e.g., rough or slippery terrain, payload...
arXiv Machine Learning
Jun 5
Is Diversity All You Need for Scalable Robotic Manipulation?
arXiv:2507. 06219v2 Announce Type: replace-cross Abstract: Data scaling has driven remarkable success in foundation models for Natural Language Processing (NLP) and Computer Vision (CV), yet the principles of effective data scaling in robotic manipulation remain insufficiently understood.
arXiv Machine Learning
Oct 8
Many Ways to Succeed: Diversity-Driven RL Fine-Tuning for VLA Generalization
arXiv:2610.09943v1 Announce Type: cross Abstract: Reinforcement learning (RL) fine-tuning improves vision-language-action (VLA) policies through closed-loop experience, yet generalization beyond the...
arXiv Machine Learning
Aug 28
Shared Actors Need Not Share Critics: Effects of Value Mismatch in Parallel Reinforcement Learning
The paper investigates the problem of sharing a single critic across multiple parallel environments in reinforcement learning. It shows that when environments assign different expected returns to the same state, a shared critic must reconcile conflicting value targets, which can distort advantage estimates and misguide policy updates. The authors propose a simple fix—providing the critic with the environment index—demonstrating through bandit models and experiments on CartPole, MuJoCo, BipedalWalker, and 16 Procgen games that this conditional critic stabilizes learning and boosts returns, achieving a 40.8% improvement in aggregate normalized return on unseen levels.
arXiv Machine Learning
Oct 21
SafeExplorer: An Unbiased Mixed-Policy Gradient for Reinforcement Learning with Recovery Interventions
arXiv:2607.08925v2 Announce Type: replace Abstract: Training agents directly on the task they will be deployed on often leads to the best policy performance, but training on physical robots can cause...
arXiv Machine Learning
Jul 13
SafeExplorer: An Unbiased Policy Gradient for Reinforcement Learning with Recovery Interventions
arXiv:2607. 08925v1 Announce Type: new Abstract: Training reinforcement-learning agents directly on physical robots makes every fall costly, since a fall can damage the platform and cannot be undone like a simulator reset; the goal is therefore to minimize falls during training rather than trade them off against return, as constrained Markov decision process (MDP) formulations do.