4 citations · 11 across the 14 of their papers we have counts for
16 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…
Robometer: Scaling General-Purpose Robotic Reward Models via Trajectory Comparisons
Anthony Liang, Yigit Korkmaz, Jiahui Zhang +14
General-purpose robot reward models are typically trained to predict absolute task progress from expert demonstrations, providing only local, frame-level supervision. While effecti…
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…
Isaac Lab: A GPU-Accelerated Simulation Framework for Multi-Modal Robot Learning
NVIDIA, :, Mayank Mittal +104
We present Isaac Lab, the natural successor to Isaac Gym, which extends the paradigm of GPU-native robotics simulation into the era of large-scale multi-modal learning. Isaac Lab c…
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,…
ReMEmbR: Building and Reasoning Over Long-Horizon Spatio-Temporal Memory for Robot Navigation
Abrar Anwar, John Welsh, Joydeep Biswas +2
Navigating and understanding complex environments over extended periods of time is a significant challenge for robots. People interacting with the robot may want to ask questions l…
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…
Feel the Bite: Robot-Assisted Inside-Mouth Bite Transfer using Robust Mouth Perception and Physical Interaction-Aware Control
Rajat Kumar Jenamani, Daniel Stabile, Ziang Liu +3
Robot-assisted feeding can greatly enhance the lives of those with mobility limitations. Modern feeding systems can pick up and position food in front of a care recipient's mouth f…
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…
Human-Robot Commensality: Bite Timing Prediction for Robot-Assisted Feeding in Groups
Jan Ondras, Abrar Anwar, Tong Wu +4
We develop data-driven models to predict when a robot should feed during social dining scenarios. Being able to eat independently with friends and family is considered one of the m…