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20242026
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cs.RO2026

SAFECAST: Robust Failure Detection for VLA Policies with Contrast-Set Training and Calibration

Harshitha Rajaprakash, Aditeya Prajapati, Rong Xue +2

Vision-language-action policies often fail under deployment-time distribution shifts such as clutter, distractor objects, lighting changes, novel objects, altered initial states, a…

cs.RO2025

Mechanistic Finetuning of Vision-Language-Action Models via Few-Shot Demonstrations

Chancharik Mitra, Yusen Luo, Raj Saravanan +7

Vision-Language Action (VLAs) models promise to extend the remarkable success of vision-language models (VLMs) to robotics. Yet, unlike VLMs in the vision-language domain, VLAs for…

cs.RO2025

RobotFleet: An Open-Source Framework for Centralized Multi-Robot Task Planning

Rohan Gupta, Trevor Asbery, Zain Merchant +2

Coordinating heterogeneous robot fleets to achieve multiple goals is challenging in multi-robot systems. We introduce an open-source and extensible framework for centralized multi-…

cs.RO2025

ReWiND: Language-Guided Rewards Teach Robot Policies without New Demonstrations

Jiahui Zhang, Yusen Luo, Abrar Anwar +5

We introduce ReWiND, a framework for learning robot manipulation tasks solely from language instructions without per-task demonstrations. Standard reinforcement learning (RL) and i…

cs.RO2025

Efficient Evaluation of Multi-Task Robot Policies With Active Experiment Selection

Abrar Anwar, Rohan Gupta, Zain Merchant +3

Evaluating learned robot control policies to determine their physical task-level capabilities costs experimenter time and effort. The growing number of policies and tasks exacerbat…

cs.RO2024

Contrast Sets for Evaluating Language-Guided Robot Policies

Abrar Anwar, Rohan Gupta, Jesse Thomason

Robot evaluations in language-guided, real world settings are time-consuming and often sample only a small space of potential instructions across complex scenes. In this work, we i…