Intelligent Robot Motion Lab
About us
We are a research group at Princeton University, led by Prof. Ani Majumdar.
Lab space and facilities
Facilities for testing our algorithms on robot hardware platforms.
Joining the lab
We are looking for exceptional PhD and postoc candidates who are excited about robotics.
News:
- Jan ’26: New survey paper on video generation models!
- Dec ’25: New preprints available!
- Z. Mei*, T. Yin, M. Baker, O.Shorinwa*, and A. Majumdar, “World Models That Know When They Don’t Know: Controllable Video Generation with Calibrated Uncertainty”
- Gemini Robotics Team, “Evaluating Gemini Robotics Policies in a Veo World Simulator”
- Oct ’25: Our work on Reliable and Scalable Robot Policy Evaluation with Imperfect Simulators won the Best Paper Award at the Eval&Deploy Workshop at CoRL!
- Oct ’25: New preprints available:
- Z. Mei*, O. Shorinwa*, and A. Majumdar, “How Confident are Video Models? Empowering Video Models to Express their Uncertainty”
- Z. Mei*, O. Shorinwa*, and A. Majumdar, “Geometry Meets Vision: Revisiting Pretrained Semantics in Distilled Fields”
- A. Badithela, D. Snyder, L. Zha, et. al., “Reliable and Scalable Robot Policy Evaluation with Imperfect Simulators”
- A. Jindal, D. Kalashnikov, et. al., “Can AI Perceive Physical Danger and Intervene?”
- A. Hancock, X. Wu, et. al., “Actions as Language: Fine-Tuning VLMs into VLAs Without Catastrophic Forgetting”
- Sept’ 25: New tech report from the Gemini Robotics Team: “Gemini Robotics 1.5: Pushing the Frontier of Generalist Robots with Advanced Embodied Reasoning, Thinking, and Motion Transfer”
- Sep’ 25: New preprint from A. Majumdar: “Deceptive Risk Minimization: Out-of-Distribution Generalization by Deceiving Distribution Shift Detectors“!
- Aug ’25: Four papers accepted to CoRL!
- T. Yin, Z. Mei, et. al., “WoMAP: World Models For Embodied Open-Vocabulary Object Localization“
- P. Sermanet, A. Majumdar, et. al., “Generating Robot Constitutions & Benchmarks for Semantic Safety”
- A. Majumdar, M. Sharma, et. al., “Predictive Red Teaming: Breaking Policies Without Breaking Robots”
- O. Shorinwa, J. Sun, et. al., “SIREN: Semantic, Initialization-Free Registration of Multi-Robot Gaussian Splatting Maps”
- July ’25: Congratulations to Dr. David Snyder for his successful PhD thesis defense: “Nonasymptotic Methods for Guaranteed Robotic Policy Synthesis and Evaluation”! Click here to see a photo of the farewell brunch.
- May ’25: Congratulations to Dr. Nathaniel Simon for his successful PhD thesis defense: “Learned Perception Modules for Autonomous Aerial Vehicle Navigation and Control”! See the photo of the celebration and farewell brunch!
- Feb ’25: Photo from our farewell brunch for Allen. We’re wishing you the best of luck on your next adventure!
- Feb ’25: Congratulations to Dr. Zhiyi “Allen” Ren for his successful PhD thesis defense: “Towards Robust and Provably Generalizable Robot Learning”! See the video and a photo of the celebration!
- Feb ’25: New preprints:
- O. Shorinwa, J. Sun, et. al., “SIREN: Semantic, Initialization-Free Registration of Multi-Robot Gaussian Splatting Maps”
- A. Majumdar, M. Sharma, et. al., “Predictive Red Teaming: Breaking Policies Without Breaking Robots”
- Jan ’25: Our work on Bring Your Own VLA (BYOVLA) has been featured in the popular press!
- Communications of the ACM: “Can LLMs Make Robots Smarter?”
- Communications of the ACM: “Images Give Robots a Sharper Focus”
- Dec ’24: New preprints:
- O. Shorinwa, Z. Mei, et. al., “A Survey on Uncertainty Quantification of Large Language Models: Taxonomy, Open Research Challenges, and Future Directions”
- A. Z. Ren, B. Ichter, and A. Majumdar, “Thinking Forward and Backward: Effective Backward Planning with Large Language Models”
- O. Shorinwa, Z. Mei, et. al., “A Survey on Uncertainty Quantification of Large Language Models: Taxonomy, Open Research Challenges, and Future Directions”
- Nov ’24: Presented our lab’s recent work at CMU RI Seminar and UT Austin Center for Autonomy Seminar!
- Nov ’24: “Perceive With Confidence: Statistical Safety Assurances for Navigation with Learning-Based Perception” published at CoRL 2024!
- Sept ’24: New preprints:
- A. Z. Ren, J. Lidard, et. al., “Diffusion Policy Policy Optimization”
- A. J. Hancock, A. Z. Ren, et. al., “Run-time Observation Interventions Make Vision-Language-Action Models More Visually Robust”
- Y. I. Liu, W. Nguyen, et. al., “Flash STU: Fast Spectral Transform Units”
- A. Farid, S. Veer, et. al., “Task-Driven Detection of Distribution Shifts with Statistical Guarantees for Robot Learning”
- May ’24: Congratulations to Dr. Meghan Booker for her successful PhD thesis defense: “Task-Driven Perception and Control for Robust and Efficient Autonomy”!
- Apr ’24: New preprints:
- A. Z. Ren, J. Clark, A. Dixit, et. al., “Explore until Confident: Efficient Exploration for Embodied Question Answering”
- A. Dixit, Z. Mei, M. Booker, et. al., “Perceive With Confidence: Statistical Safety Assurances for Navigation with Learning-Based Perception”
- J. Lidard, H. Pham, A. Bachman, et. al., “Risk-Calibrated Human-Robot Interaction via Set-Valued Intent Prediction”
- Apr ’24: Our work has been featured in the popular press!
- Science News Explores: “How to design artificial intelligence that acts nice — and only nice”
- Apr ’24: Congratulations to Allen Ren for winning the Jacobus Fellowship!
- Feb ’24: Our work has been featured in the popular press!
Older News
- Dec ’23: Our work on Robots That Ask For Help has been featured in the popular press!
- MIT Technology Review (paywall): “These Robots Know When to Ask for Help”
- Princeton Engineering: “How do you make a robot smarter? Program it to know what it doesn’t know.”
- Dec ’23: New preprints:
- N. Simon and A. Majumdar, “MonoNav: MAV Navigation via Monocular Depth Estimation and Reconstruction” (accepted to ISER)
- R. Firoozi, J. Tucker, S. Tian, A. Majumdar, et. al., “Foundation Models in Robotics: Applications, Challenges, and the Future”
- Dec ’23: “PAC-Bayes Generalization Certificates for Learned Inductive Conformal Prediction” published at Advances in Neural Information Processing Systems (NeurIPS), 2023
- Nov ’23: Best Student Paper Award at CoRL 2023 for our paper “Robots That Ask For Help: Uncertainty Alignment for Large Language Model Planners”!
- Nov ’23: Congratulations to Allen Ren for winning the SEAS Award for Excellence!
- Sep ’23: New preprints:
- J. Gao, B. Sarkar, F. Xia, T. Xiao, J. Wu, B. Ichter, A. Majumdar, and D. Sadigh, “Physically Grounded Vision-Language Models”
- E. Lepowsky*, D. Snyder*, A. Glaser, and A. Majumdar, “Privacy-Preserving Absence Confirmation in Sensitive Nuclear Facilities” (* equal contribution)
- May ’23: Congratulations to Dr. Vincent Pacelli for his successful PhD thesis defense: “Information-Theoretic Necessary and Sufficient Conditions for the Task-Driven
Control of Robots”! - Aug ’23: Two papers accepted to the International Journal of Robotics Research (IJRR)!
- A. Majumdar, Z. Mei, and V. Pacelli, “Fundamental Limits for Sensor-Based Robot Control”
- S. Singh, B. Landry, A. Majumdar, J.-J. Slotine, and M. Pavone, “Robust Feedback Motion Planning via Contraction Theory”
- Aug ’23: Three papers accepted to CoRL!
- A. Z. Ren, H. Dai, B. Burchfiel, and A. Majumdar, “AdaptSim: Task-Driven Simulation Adaptation for Sim-to-Real Transfer”. See video.
- A. Z. Ren, et. al., “Robots That Ask For Help: Uncertainty Alignment for Large Language Model Planners.” See video and webpage.
- D. Snyder, M. Booker, N. Simon, W. Xia, D. Suo, E. Hazan, and A. Majumdar, “Online Learning for Obstacle Avoidance.”
- Jul ’23: New preprints:
- A. Z. Ren, et. al., “Robots That Ask For Help: Uncertainty Alignment for Large Language Model Planners.” See video and webpage. In collaboration with Google DeepMind.
- D. Snyder, M. Booker, N. Simon, W. Xia, D. Suo, E. Hazan, and A. Majumdar, “Online Learning for Obstacle Avoidance.” In collaboration with Google DeepMind.
- Jul ’23: Congrats to Zhiting Mei for winning the Phillips Fellowship!
- Jul ’23: Congrats to Nate Simon, Sasha Bodrova, and An-Ya Olson for receiving the Crocco Award for Teaching Excellence for MAE 345/549: Introduction to Robotics!
- May ’23: Congratulations to Dr. Vincent Pacelli for his successful PhD thesis defense: “Information Theoretic Necessary and Sufficient Conditions for Task-Driven Control of Robots”!
- May ’23: Outstanding Presentation Award at Princeton Research Day! See the video.
- Apr ’23: Paper accepted to ICML!
- Mar ’23: Two papers accepted to ICRA!
- M. Booker and A. Majumdar, “Switching Attention in Time-Varying Environments via Bayesian Inference of Abstractions”.
- N. Simon, A. Z. Ren, A. Piqué, D. Snyder, D. Barretto, M. Hultmark, and A. Majumdar, “FlowDrone: Wind Estimation and Gust Rejection on UAVs Using Fast-Response Hot-Wire Flow Sensors”.
- Feb ’23: Prof. Anirudha Majumdar awarded a Sloan Fellowship!
- Feb ’23: Collaboration work with Karthik Narasimhan’s group on leveraging language information for tool manipulation is featured in the article “Machines Learn Better if We Teach Them the Basics” in Quanta Magazine!
- Feb ’23: New preprints:
- A. Z. Ren, H. Dai, B. Burchfiel, and A. Majumdar, “AdaptSim: Task-Driven Simulation Adaptation for Sim-to-Real Transfer”. See video. In collaboration with Toyota Research Institute (TRI).
- A. Majumdar, Z. Mei, and V. Pacelli, “Fundamental Limits for Sensor-Based Robot Control”.
- Jan ’23: Congratulations to Dr. Alec Farid for his successful PhD thesis defense: “Provably Safe Learning-Based Robot Control via Anomaly Detection and Generalization Theory”! See video.
- Dec ’22: We took delivery of our Windshape! This dynamic fan array uses 324 individually controllable fans to enable precise construction of spatially-varying gusts reaching upwards of 16 m/s. We will be using it for research on UAV control in extreme wind conditions.
- Nov ’22: New preprint:
- M. Booker and A. Majumdar, “Switching Attention in Time-Varying Environments via Bayesian Inference of Abstractions”. See video.
- Nov ’22: Paper accepted to Measurement Science and Technology (MST)!
- N. Simon*, A. Piqué*, D. Snyder, K. Ikuma, A. Majumdar, and M. Hultmark, “Fast-Response Hot-wire Flow Sensors for Wind and Gust Estimation on UAVs” (*Equal contribution).
- K.-C. Hsu*, A. Z. Ren*, D. P. Nguyen, A. Majumdar**, and J. F. Fisac**, “Sim-to-Lab-to-Real: Safe Reinforcement Learning with Shielding and Generalization Guarantees” (*Equal contributions, **Equal advising). Oct ’22: Paper published at Artificial Intelligence Journal (AIJ)!
- Oct ’22: New pre-print:
- N. Simon, A. Z. Ren, A. Piqué, D. Snyder, D. Barretto, M. Hultmark, and A. Majumdar, “FlowDrone: Wind Estimation and Gust Rejection on UAVs Using Fast-Response Hot-Wire Flow Sensors”. See video.
- Sep ’22: Congrats to Alec Farid for receiving the School of Engineering and Applied Science (SEAS) Award for Excellence!
- Sep ’22: ONR Young Investigator Program award.
- Sep ’22: New pre-print:
- N. Simon, A. Piqué, D. Snyder, K. Ikuma, A. Majumdar, and M. Hultmark, “Fast-Response Hot-wire Flow Sensors for Wind and Gust Estimation on UAVs”.
- Sep ’22: Paper accepted to CoRL!
- A. Z. Ren, B. Govil, T.-Y. Yang, K. Narasimhan*, and A. Majumdar*, “Leveraging Language for Accelerated Learning of Tool Manipulation”.
- Jun ’22: Congrats to Allen Ren for receiving the Harari Fellowship from the MAE department!
- Jun ’22: Congrats to David Snyder and Allen Ren for receiving the Crocco Award for Teaching Excellence for MAE 345/549: Introduction to Robotics!
- Jun ’22: New pre-print:
- A. Z. Ren, B. Govil, T.-Y. Yang, K. Narasimhan*, and A. Majumdar*, “Leveraging Language for Accelerated Learning of Tool Manipulation”.
- Apr ’22: Two papers accepted to RSS!
- A. Farid*, D. Snyder*, A. Z. Ren, and A. Majumdar, “Failure Prediction with Statistical Guarantees for Vision-Based Robot Control”.
- A. Majumdar and V. Pacelli, “Fundamental Performance Limits for Sensor-Based Robot Control and Policy Learning”.
- Apr ’22: Paper accepted to WAFR!
- M. Ho*, A. Farid*, and A. Majumdar, “Comparing the Complexity of Robotics Tasks”.
- Feb ’22: Our work on using airflow sensors for improving drone control in collaboration with Marcus Hultmark’s group was highlighted by Princeton Engineering news.
- Feb ’22: New preprints from our group:
- M. Ho*, A. Farid*, and A. Majumdar, “Comparing the Complexity of Robotics Tasks”.
- A. Farid*, D. Snyder*, A. Z. Ren, and A. Majumdar, “Failure Prediction with Statistical Guarantees for Vision-Based Robot Control”.
- A. Majumdar and V. Pacelli, “Fundamental Performance Limits for Sensor-Based Robot Control and Policy Learning”.
- Jan ’22: We have 3 papers from our group accepted to ICRA 2022!
- Jan ’22: New preprint in collaboration with Jaime Fisac’s group:
- K.-C. Hsu*, A. Z. Ren*, D. P. Nguyen, A. Majumdar**, and J. F. Fisac**, “Sim-to-Lab-to-Real: Safe Reinforcement Learning with Shielding and Generalization Guarantees” (*Equal contributions, **Equal advising).
- Jan ’22: Paper accepted to the Robotics and Automation Letters (RA-L) journal:
- A. Z. Ren, and A. Majumdar, “Distributionally Robust Policy Learning via Adversarial Environment Generation”.
- Nov ’21: Ani is presenting the group’s research at UC Berkeley, UCSD, and Waymo. A video of the UCSD seminar is available online.
- Nov ’21: Two new preprints from the group:
- A. Agarwal, S. Veer, A.Z. Ren, and A. Majumdar, “Stronger Generalization Guarantees for Robot Learning by Combining Generative Models and Real-World Data”.
- A. E. Gurgen, A. Majumdar, and S. Veer, “Learning Provably Robust Motion Planners using Funnel Libraries”.
- Oct ’21: Paper accepted to NeurIPS:
- A. Farid and A. Majumdar, “Generalization Bounds for Meta-Learning via PAC-Bayes and Uniform Stability”.
- Sep ’21: New preprint from our group:
- V. Pacelli and A. Majumdar, “Robust Control under Uncertainty via Bounded Rationality and Differential Privacy”.
- Sep ’21: Paper accepted to the Conference on Robot Learning (CoRL):
- A. Farid*, S. Veer*, and A. Majumdar, “Task-Driven Out-of-Distribution Detection with Statistical Guarantees for Robot Learning”.
- Aug ’21: Alec Farid received the Dale Grieb Safety Award from the School of Engineering and Applied Science at Princeton. Congrats Alec!
- Jun ’21: New preprint from our group:
- A. Z. Ren, and A. Majumdar, “Distributionally Robust Policy Learning via Adversarial Environment Generation”.
- Jun ’21: Meghan Booker and Alec Farid received the Crocco Award for Teaching Excellence for the course MAE 345/549: Introduction to Robotics (Fall ’20). Alexandra (Sasha) Bodrova and Nate Simon received the Guggenheim Second Year Fellowships. Congrats Meghan, Alec, Sasha, and Nate!
- May ’21: Paper accepted to ICML:
- N. Agarwal, E. Hazan, A. Majumdar, and K. Singh, “A Regret Minimization Approach to Iterative Learning Control”.
- Apr ’21: Our group’s research is featured in the A.I. Nation Podcast from WHYY (NPR/PBS affiliate) and Princeton. The new podcast series was highlighted on Princeton’s homepage.
- Mar ’21: We have three papers accepted to L4DC:
- U. Ghai*, D. Snyder*, A. Majumdar, and E. Hazan, “Generating Adversarial Disturbances for Controller Verification”. (Selected for oral presentation)
- M. Booker and A. Majumdar, “Learning to Actively Reduce Memory Requirements for Robot Control Tasks”.
- A. Sonar, V. Pacelli, and A. Majumdar, “Invariant Policy Optimization: Towards Stronger Generalization in Reinforcement Learning”.
- Feb ’21: NSF CAREER award.
- Feb ’21: New preprints from our group. Feedback is welcome!
- U. Ghai*, D. Snyder*, A. Majumdar, and E. Hazan, “Generating Adversarial Disturbances for Controller Verification”.
- A. Farid and A. Majumdar, “PAC-BUS: Meta-learning Bounds via PAC-Bayes and Uniform Stability”.
- Feb ’21: We are delighted to have received an Innovation Fund grant from Princeton SEAS for a collaborative project with Marcus Hultmark’s group.
- Jan ’21: Young Faculty Researcher Award from the Toyota Research Institute.
- Nov ’20: Our work on safety and generalization guarantees for learning-based control of robots was highlighted by Princeton Engineering in a news article:
- Oct ’20: Two papers accepted to the 2020 Conference on Robot Learning (CoRL):
- “Generalization Guarantees for Multi-Modal Imitation Learning”. Work led by Allen Ren and Sushant Veer.
- “Probably Approximately Correct Vision-Based Planning using Motion Primitives”. Work led by Sushant Veer.
- Aug ’20: Our paper “PAC-Bayes Control: Learning Policies that Provably Generalize to Novel Environments” was accepted to the International Journal of Robotics Research (IJRR)!
- Aug ’20: New preprints from our group. Feedback is welcome!
- “Generalization Guarantees for Multi-Modal Imitation Learning”. Work led by Allen Ren and Sushant Veer.
- “Invariant Policy Optimization: Towards Stronger Generalization in Reinforcement Learning”. Work led by Anoop Sonar and Vince Pacelli.
- “Learning to Actively Reduce Memory Requirements for Robot Control Tasks”. Work led by Meghan Booker.
- July ’20: Sushant Veer has a new preprint “CoNES: Convex Natural Evolutionary Strategies” on high-dimensional blackbox optimization. Feedback is welcome.
- July ’20: Vince Pacelli was awarded the Crocco Award for Teaching Excellence. This prize was awarded by the MAE Department in recognition of outstanding performance as an Assistant in Instruction for “MAE 345: Introduction to Robotics”, in Fall 2019. Congrats Vince!
- May ’20: Our paper “Learning Task-Driven Control Policies via Information Bottlenecks” was accepted to RSS 2020! Vince’s 5-min talk on the paper is available on YouTube.
- May ’20: Alfred Rheinstein Faculty Award.
- Apr ’20: We are excited to have received a second Amazon Research Award.
- Feb ’20: Sushant Veer has a new preprint “Probably Approximately Correct Vision-Based Planning using Motion Primitives”. Feedback is welcome.
- Feb ’20: We are excited to have received another Google Faculty Research Award.
- Feb ’20: Vince Pacelli has a new preprint “Learning Task-Driven Control Policies via Information Bottlenecks”. Feedback is welcome.
- Feb ’20: Ani’s talk on “Safety and Generalization Guarantees for Learning-Based Control of Robots” at the Penn GRASP seminar is available on YouTube.
- Oct ’19: Ani is presenting the lab’s recent work this Fall at the MIT Robotics Seminar, Penn GRASP Seminar, Johns Hopkins Applied Physics Lab, Yale Mechanical Engineering and Materials Science seminar, and the Amazon Research Awards symposium.
- Sep ’19: New paper with Sumeet Singh, Benoit Landry, Jean-Jacques Slotine, and Marco Pavone: “Robust Feedback Motion Planning via Contraction Theory”
- Sep ’19: New survey paper with Georgina Hall and Amir Ali Ahmadi on scalable approaches to semidefinite programming
- July ’19: Extended version of paper on PAC-Bayes control available on ArXiv
- Jan ’19: Paper accepted to ICRA 2019: Check out our paper entitled “Task-Driven Estimation and Control via Information Bottlenecks”
- Jan ’19: Amazon Research Award
- Sep ’18: Paper accepted to CoRL 2018: Check out our paper entitled “PAC-Bayes Control: Synthesizing Controllers that Provably Generalize to Novel Environments”
- Jun ’18: Paper of the Year Award from the International Journal of Robotics Research (IJRR) for funnel library paper with Russ Tedrake
- Apr ’18: NSF CRII Award
- Mar ’18: Google Faculty Research Award