2 citations · 3 across the 10 of their papers we have counts for
11 papers
SWAP: Stepwise Action Policy Routing for Vision-Language-Action Models
Mousumi Das, Aditeya Prajapati, Abrar Anwar +1
Robot manipulation systems using Vision-Language-Action (VLA) model backbones typically use just one VLA for task execution. However, individual VLAs do not perform well across dif…
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…
DiPS: Dialogue Policy Selection for High-Stakes Persuasion Agents
Tianyi Zhang, Mousumi Das, Abrar Anwar +2
Large Language Models (LLMs) often struggle with persuasion in high-stakes scenarios. People's individual personalities and concerns require tailored strategies rather than a one-s…
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…
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-…
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…
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…
M3PT: A Transformer for Multimodal, Multi-Party Social Signal Prediction with Person-aware Blockwise Attention
Yiming Tang, Abrar Anwar, Jesse Thomason
Understanding social signals in multi-party conversations is important for human-robot interaction and artificial social intelligence. Social signals include body pose, head pose,…
Generating Contextually-Relevant Navigation Instructions for Blind and Low Vision People
Zain Merchant, Abrar Anwar, Emily Wang +2
Navigating unfamiliar environments presents significant challenges for blind and low-vision (BLV) individuals. In this work, we construct a dataset of images and goals across diffe…
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…
Which One? Leveraging Context Between Objects and Multiple Views for Language Grounding
Chancharik Mitra, Abrar Anwar, Rodolfo Corona +3
When connecting objects and their language referents in an embodied 3D environment, it is important to note that: (1) an object can be better characterized by leveraging comparativ…