arXiv:2511. 06667v2 Announce Type: replace-cross Abstract: With the explosive growth of rigid-body simulators, policy learning in simulation has become the de facto standard for most rigid morphologies.
By Andrew Choi, Dezhong Tong, Xiaonan Huang
arXiv:2602.00532v2 Announce Type: replace-cross
Abstract: Constraint handling is central to constrained black-box optimization (BBO), where objective improvement and feasibility restoration often pro...
By Sijie Ma, Zeyuan Ma, Yue-Jiao Gong, Ran Cheng
arXiv:2605. 26452v2 Announce Type: replace-cross Abstract: Safe reinforcement learning (RL) for robotic systems requires policies that improve task performance while satisfying state and input constraints during both training and deployment.
By Dhruv S. Kushwaha, Zoleikha A. Biron
arXiv:2606. 24039v1 Announce Type: cross Abstract: Robotics increasingly relies on GPUs for parallel simulation, large-scale learning, and neural-network inference.
By Gabriel Bravo-Palacios, Jianghan Zhang, Zachary Pestrikov, Brian Plancher, Thomas Lew
arXiv:2604. 12474v3 Announce Type: replace-cross Abstract: In many robotic tasks, agents must traverse a sequence of spatial regions to complete a mission.
By Lidor Erez, Shahaf S. Shperberg, Ayal Taitler
arXiv:2602. 04132v4 Announce Type: replace-cross Abstract: Reinforcement Learning (RL) has achieved remarkable success in solving complex sequential decision-making problems.
By Dhruv S. Kushwaha, Zoleikha A. Biron