8 papers
Progress Probes : Lightweight Runtime Monitoring for Vision-Language-Action Models
Atiksh Bhardwaj, Edward W. Duan, Junwon Seo +4
Vision-Language-Action (VLA) models can perform diverse manipulation tasks directly from visual observations and natural language instructions, but they remain prone to mistakes an…
Decoding Task Progress from VLA Representations
Atiksh Bhardwaj, Edward Weiyi Duan, Prithwish Dan +2
Vision-language-action models (VLAs) are moving rapidly towards deployment as general-purpose manipulation policies, but we currently lack basic tools for understanding what these…
A Minimalist Retargeting-Guided Reinforcement Learning Recipe for Dexterous Manipulation
Yunhai Feng, Natalie Leung, Jiaxuan Wang +3
Recent work in humanoid whole-body control has found success with a simple recipe: retarget human motion to robot kinematic references, then train policies via reinforcement learni…
Scaling Pretrained Representations Enables Label-Free Out-of-Distribution Detection Without Fine-Tuning
Brett Barkley, Preston Culbertson, David Fridovich-Keil
Models trained with deep learning often fail to signal when inputs fall outside their training data manifold, leading to unreliable predictions under distribution shift. Prior work…
Sumo: Dynamic and Generalizable Whole-Body Loco-Manipulation
John Z. Zhang, Maks Sorokin, Jan Brüdigam +14
This paper presents a sim-to-real approach that enables legged robots to dynamically manipulate large and heavy objects with whole-body dexterity. Our key insight is that by perfor…
TransMASK: Masked State Representation through Learned Transformation
Sagar Parekh, Preston Culbertson, Dylan P. Losey
When humans learn new manipulation skills, they are able to generalize these skills to new contexts and environments. In particular, when learning, humans can easily separate task-…
SCOPED: Score-Curvature Out-of-distribution Proximity Evaluator for Diffusion
Brett Barkley, Preston Culbertson, David Fridovich-Keil
Out-of-distribution (OOD) detection is essential for reliable deployment of machine learning systems in vision, robotics, reinforcement learning, and beyond. We introduce Score-Cur…
Generative Models From and For Sampling-Based MPC: A Bootstrapped Approach For Adaptive Contact-Rich Manipulation
Lara Brudermüller, Brandon Hung, Xinghao Zhu +4
We present a generative predictive control (GPC) framework that amortizes sampling-based Model Predictive Control (SPC) by bootstrapping it with conditional flow-matching models tr…