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cs.RO2026
FlowDPG: Deterministic Policy Gradient on Flow Matching Policies for Real-World Manipulation
Kexin Shi, Junyao Shi, Poorvi Hebbar +5
Real-world reinforcement learning for robotic manipulation remains challenging, and this difficulty is amplified for flow matching policies: applying policy gradient methods to the…
cs.RO2025
Local Policies Enable Zero-shot Long-horizon Manipulation
Murtaza Dalal, Min Liu, Walter Talbott +4
Sim2real for robotic manipulation is difficult due to the challenges of simulating complex contacts and generating realistic task distributions. To tackle the latter problem, we in…
cs.RO2024
Continuously Improving Mobile Manipulation with Autonomous Real-World RL
Russell Mendonca, Emmanuel Panov, Bernadette Bucher +2
We present a fully autonomous real-world RL framework for mobile manipulation that can learn policies without extensive instrumentation or human supervision. This is enabled by 1)…