26 papers
ELASTIC: Efficiently Learning to Adaptively Scale Test-Time Compute for Generative Control Policies
Andrew Zou Li, Gokul Swamy, Yonatan Bisk +1
Generative control policies (GCPs), such as diffusion policies and flow-based vision-language-action models, enable test-time scaling in robot control. Test-time compute can be all…
WEAVER, Better, Faster, Longer: An Effective World Model for Robotic Manipulation
Arnav Kumar Jain, Yilin Wu, Jesse Farebrother +2
The potential impacts of world models (WMs, i.e., learned simulators) on robotics are far-reaching -- policy evaluation, policy improvement, and test-time planning -- all with limi…
Inference-time Policy Steering via Vision and Touch
Yilin Wu, Zilin Si, Zeynep Temel +2
Inference-time steering adapts pre-trained generative robot policies during deployment by verifying candidate actions before execution. While prior methods typically perform this v…
Learning What to Say to Your VLA: Mostly Harmless Vision Language Action Model Steering
Hyun Joe Jeong, Gokul Swamy, Andrea Bajcsy
Vision-Language-Action (VLA) models provide a natural language interface to robot control, but the mapping from language to behavior is often brittle and unintuitive: semantically…
How Well Do Latent World Models Understand Partially Observable Safety Constraints?
Matthew Kim, Kensuke Nakamura, Andrea Bajcsy
Latent world models are a promising approach for learning state representations and dynamics directly from high-dimensional observations, enabling robot control in hard-to-model se…
Position: Good Embodied Reward Models Need Bad Behavior Data
Ran Tian, Yilin Wu, Andrea Bajcsy
This position paper argues that to obtain reliable embodied reward models, the community must invest in ``bad'' robot data: failed, suboptimal, error-prone, and even hazardous beha…