7 papers
Warp RL: Reshaping Base Policy Distributions for Dynamics Adaptation
Ethan Hirschowitz, Fabio Ramos
The paper introduces Warp RL, a method that adapts a pretrained robot policy by applying an invertible, state‑conditioned transformation to its action distribution, enabling shape…
Learning to Plan & Schedule with Reinforcement-Learned Bimanual Robot Skills
Weikang Wan, Fabio Ramos, Xuning Yang +1
Long-horizon contact-rich bimanual manipulation presents a significant challenge, requiring complex coordination involving a mixture of parallel execution and sequential collaborat…
Do What You Say: Steering Vision-Language-Action Models via Runtime Reasoning-Action Alignment Verification
Yilin Wu, Anqi Li, Tucker Hermans +3
Reasoning Vision Language Action (VLA) models improve robotic instruction-following by generating step-by-step textual plans before low-level actions, an approach inspired by Chain…
Informing Acquisition Functions via Foundation Models for Molecular Discovery
Qi Chen, Fabio Ramos, Alán Aspuru-Guzik +1
Bayesian Optimization (BO) is a key methodology for accelerating molecular discovery by estimating the mapping from molecules to their properties while seeking the optimal candidat…
Harnessing Bounded-Support Evolution Strategies for Policy Refinement
Ethan Hirschowitz, Fabio Ramos
Improving competent robot policies with on-policy RL is often hampered by noisy, low-signal gradients. We revisit Evolution Strategies (ES) as a policy-gradient proxy and localize…
VLA-0: Building State-of-the-Art VLAs with Zero Modification
Ankit Goyal, Hugo Hadfield, Xuning Yang +2
Vision-Language-Action models (VLAs) hold immense promise for enabling generalist robot manipulation. However, the best way to build them remains an open question. Current approach…