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2023 and Earlier
| Nov 2023 | My work on efficient robot evaluation was accepted as an oral presentation at the CoRL 2023 LangRob Workshop! |
| Feb 2023 | I was awarded a $100k fellowship as a Horatio Algers Graduate Scholar. |
| Dec 2022 | I will be presenting my work on Human-Robot Commensality at CoRL 2022! |
| July 2022 | My work on social bite timing was featured on the New Scientist! |
| May 2022 | I will be a Visiting Scholar at Cornell University with Tapo Bhattacharjee to work on robot-assisted dining in social settings |
| June 2021 | I will be presenting our work on gaze for social navigation at ICRA 2021 |
| May 2021 | I defended my honors thesis on deep reinforcement learning for mesh refinement and finally graduated from UT Austin |
| May 2021 | I will be working with Tapo Bhattacharjee from Cornell this summer thanks to the Google exploreCSR program |
| May 2021 | I will be joining the University of Southern California for my PhD to work on HRI and language grounding with Jesse Thomason |
| Jan 2021 | I attended the AAAI-21 Undergraduate Consortium, and met so many wonderful people while presenting my research on spiking weight-agnostic neural networks |
| Jan 2021 | I will be participating in Google Research's CS Research Mentorship program |
| July 2020 | I (virtually) attended my first conference, ICONS, and presented a poster on spiking neural networks |
research |
2025 | |
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Mechanistic Finetuning of Vision-Language-Action Models via Few-Shot Demonstrations
Chancharik Mitra*, Yusen Luo*, Raj Saravanan*, Dantong Niu, Anirudh Pai, Jesse Thomason, Trevor Darrell, Abrar Anwar, Deva Ramanan, Roei Herzig Robotic Steering is a mechanistic, few-shot finetuning approach for vision-language-action models that selectively adapts task-specific attention heads, achieving more robust, efficient, and interpretable robot learning than LoRA across diverse tasks. In Submission arxiv |
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Isaac Lab: A GPU-Accelerated Simulation Framework for Multi-Modal Robot Learning
NVIDIA + me! I helped introduce approaches for VLA post-training with IsaacLab! Whitepaper arxiv |
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RobotFleet: An Open-Source Framework for Centralized Multi-Robot Task Planning
Rohan Gupta*, Trevor Asbery*, Zain Merchant*, Abrar Anwar, Jesse Thomason RobotFleet is an open-source, extensible framework that introduces a centralized, modular autonomy stack to simplify scalable planning, scheduling, and execution for heterogeneous multi-robot fleets in open-world tasks. Multi-Robot Systems Workshop @ RSS 2025. arxiv code |
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ReWiND: Language-Guided Rewards Teach Robot Policies without New Demonstrations
Jiahui Zhang*, Yusen Luo*, Abrar Anwar*, Sumedh Sontakke, Joseph Lim, Jesse Thomason, Erdem Biyik, Jesse Zhaing We design ReWiND rewards that use language-guided rewards to train bimanual arms on OOD tasks in 1 hour! We use offline-to-online, lang-conditioned, visual RL on action-chunked transformers on a real robot and in simulation! CoRL 2025 (Oral Presentation) RoboReps Workshop @ RSS 2025 (Best paper nominee) OOD Workshop @ RSS 2025 (Best paper) arxiv website twitter |
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Efficient Evaluation of Multi-Task Robot Policies With Active Experiment Selection
Abrar Anwar*, Rohan Gupta, Zain Merchant, Sayan Ghosh, Willie Neiswanger, Jesse Thomason The space of language commands a robot can execute grows combinatorially with scene complexity. Evaluating a robot on this large domain is impractical + takes time, so we introduce contrast sets for robots to make small perturbations to test instances. This leads to good test set estimation and less experimenter effort. CoRL 2025 RobotEvaluation Workshop @ RSS 2025 arxiv twitter |
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ReMEmbR: Building and Reasoning Over Long-Horizon Spatio-Temporal Memory for Robot Navigation
Abrar Anwar*, John Welsh, Joydeep Biswas, Soha Pouya, Yan Chang To tackle long-horizon spatio-temporal memory, we introduce a retrieval-based approach for building and reasoning over memory. We show improved performance on planning and embodied question answering given a long video history. ICRA 2025 arxiv code website blog twitter 
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M3PT: A Transformer for Multimodal, Multi-Party Social Signal Prediction with Person-aware Blockwise Attention
Yiming Tang, Abrar Anwar, Jesse Thomason M3PT is a causal multimodal transformer that jointly models social signals across multiple participants and modalities, improving prediction of behaviors like speaking and bite timing in multi-party human interactions. Nonverbal Cues Workshop @ ICRA 2025 (Best paper) arxiv code |
2024 | |
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Contrast Sets for Evaluating Language-Guided Robot Policies
Abrar Anwar*, Rohan Gupta*, Jesse Thomason The space of language commands a robot can execute grows combinatorially with scene complexity. Evaluating a robot on this large domain is impractical + takes time, so we introduce contrast sets for robots to make small perturbations to test instances. This leads to good test set estimation and less experimenter effort. CoRL 2024 arxiv twitter |
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Which One? Leveraging Context Between Objects and Multiple Views for Language Grounding
Chancharik Mitra*, Abrar Anwar*, Rodolfo Corona, Dan Klein, Trevor Darrell, Jesse Thomason We present the MAGiC model which selects an object referent based on language meant to distinguish between two similar objects by reasoning over both objects from multiple vantage points. NAACL 2024 paper arxiv code |
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Generating Contextually-Relevant Navigation Instructions for Blind and Low Vision People
Zain Merchant, Abrar Anwar, Emily Wang, Souti Chattopadhyay, Jesse Thomason Navigating unfamiliar environments presents significant challenges for blind and low-vision (BLV) individuals. We investigate how grounded instruction generation methods can provide contextually-relevant navigational guidance to BLV users. ROMAN 2024 Late Breaking Report (LBR) ROMAN 2024 Interactive AI Workshop (Best paper) arxiv |
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Feel the Bite: Robot-Assisted Inside-Mouth Bite Transfer using Robust Mouth Perception and Physical Interaction-Aware Control
Rajat Jenamani, Daniel Stabile, Ziang Liu, Abrar Anwar, Katherine Dimitropoulou, Tapomayukh Bhattacharjee We design a system to feed people with disabilities in their mouth using real-time mouth perception and tactile-informed control. HRI 2024. (Best paper nominee) arxiv video website |
2023 | |
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Exploring Strategies for Efficient VLN Evaluation
Abrar Anwar*, Rohan Gupta*, Elle Szabo, Jesse Thomason Evaluation in the real world is often time-consuming and expensive, so we propose a targeted contrast set-based evaluation strategy to efficiently evaluate the linguistic and visual capabilities of an end-to-end VLN policy. Workshop on Language and Robot Learning (LangRob) @ CoRL 2023. (Oral Presentation) paper |
2022 | |
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Human-Robot Commensality: Bite Timing Prediction for Robot-Assisted Feeding in Groups
Jan Ondras*, Abrar Anwar*, Tong Wu*, Fanjun Bu, Malte Jung, Jorge Jose Ortiz, Tapomayukh Bhattacharjee We develop data-driven models to predict when a robot should feed during social dining scenarios. We build a dataset of human-human commensality, develop novel models to learn social dynamics of when to feed, and conduct a human-robot commensality study. CoRL 2022 paper arxiv code website |
2021 | |
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Deep Reinforcement Learning for Optimal Refinement of Cross-Sectional Mesh Sequence Finite Elements
Abrar Anwar Developed the first deep reinforcement learning framework for mesh refinement and refined âgoodâ quality surface reconstructions of cross-sectional contours using soft-actor critic Honors Thesis, 2021 |
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Watch Where You're Going! Gaze and Head Orientation as Predictors for Social Robot Navigation
Blake Holman, Abrar Anwar, Akash Singh, Mauricio Tec, Justin Hart, Peter Stone We leverage virtual reality to collect gaze and position data to create a predictive model and a mixed effects model to show gaze orientation precedes other features ICRA 2021 paper video |
2020 | |
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Evolving Spiking Circuit Motifs using Weight Agnostic Neural Networks.
Abrar Anwar, Craig Vineyard, William Severa, Srideep Musuvathy, Suma Cardwell An evolutionary, weight agnostic method is used to generate spiking neural networks used for classification, control, and various other tasks AAAI-21 Undergraduate Consortium , 2021 Computer Science Research Institute Summer Proceedings, 2020 International Conference on Neuromorphic Systems 2020 (poster) paper tech report poster |
2019 | |
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BrainSLAM: Robust autonomous navigation in sensor-deprived contexts
Felix Wang, James B. Aimone, Abrar Anwar, Srideep Musuvathy. We explore using brain-inspired approaches to navigation and localization in a noisy, data-sparse environment for a hypersonic glide vehicle. Rotation invariant feature representations are used to increase accuracy and reduce map storage Sandia National Labs Technical Report, 2019 tech report |
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Design and source code from Jon Barron's website |