180 citations · 578 across the 40 of their papers we have counts for
46 papers
FCTS: Failure-Guided Curriculum Tree Search Over Generated Terrain for Legged Parkour
Narayanan Palghat Parameswaran, Dong-Ki Kim, Jay Patrikar +4
Quadruped parkour policies handle canonical obstacles well, but their training curricula often miss the irregular, compound geometry of industrial and disaster-response sites, wher…
PriMoRetarget: Humanoid Motion Transfer Through Prioritized Motion Retargeting
Nicholas Ioannidis, Dun Yang, Max Smith +5
Motion retargeting is a critical preprocessing step for transferring human motion data to humanoid robots. While existing methods are effective for single-character motions, intera…
PhyMotion: Structured 3D Motion Reward for Physics-Grounded Human Video Generation
Yidong Huang, Zun Wang, Han Lin +6
Generating realistic human motion is a central yet unsolved challenge in video generation. While reinforcement learning (RL)-based post-training has driven recent gains in general…
Simulation Distillation: Pretraining World Models in Simulation for Rapid Real-World Adaptation
Jacob Levy, Tyler Westenbroek, Kevin Huang +6
Robot learning requires adaptation methods that improve reliably from limited, mixed-quality interaction data. This is especially challenging in long-horizon, contact-rich tasks, w…
World Model Failure Classification and Anomaly Detection for Autonomous Inspection
Michelle Ho, Muhammad Fadhil Ginting, Isaac R. Ward +4
Autonomous inspection robots for monitoring industrial sites can reduce costs and risks associated with human-led inspection. However, accurate readings can be challenging due to o…
Delay-Aware Diffusion Policy: Bridging the Observation-Execution Gap in Dynamic Tasks
Aileen Liao, Dong-Ki Kim, Max Olan Smith +2
As a robot senses and selects actions, the world keeps changing. This inference delay creates a gap of tens to hundreds of milliseconds between the observed state and the state at…
GrndCtrl: Grounding World Models via Self-Supervised Reward Alignment
Haoyang He, Jay Patrikar, Dong-Ki Kim +5
Recent advances in video world modeling have enabled large-scale generative models to simulate embodied environments with high visual fidelity, providing strong priors for predicti…
Planning with Sketch-Guided Verification for Physics-Aware Video Generation
Yidong Huang, Zun Wang, Han Lin +5
Recent video generation approaches increasingly rely on planning intermediate control signals such as object trajectories to improve temporal coherence and motion fidelity. However…
Co-Me: Confidence-Guided Token Merging for Visual Geometric Transformers
Yutian Chen, Yuheng Qiu, Ruogu Li +4
We propose Confidence-Guided Token Merging (Co-Me), an acceleration mechanism for visual geometric transformers without retraining or finetuning the base model. Co-Me distilled a l…
Don't Run with Scissors: Pruning Breaks VLA Models but They Can Be Recovered
Jason Jabbour, Dong-Ki Kim, Max Smith +6
Vision-Language-Action (VLA) models are a promising paradigm for field robotics, but their deployment is bottlenecked by strict edge SWaP-C constraints. Compression techniques such…
VENTURA: Adapting Image Diffusion Models for Unified Task Conditioned Navigation
Arthur Zhang, Xiangyun Meng, Luca Calliari +5
Robots must adapt to diverse human instructions and operate safely in unstructured, open-world environments. Recent Vision-Language models (VLMs) offer strong priors for grounding…
StageACT: Stage-Conditioned Imitation for Robust Humanoid Door Opening
Moonyoung Lee, Dong Ki Kim, Jai Krishna Bandi +4
Humanoid robots promise to operate in everyday human environments without requiring modifications to the surroundings. Among the many skills needed, opening doors is essential, as…
Multi-Agent Reinforcement Learning for Sample-Efficient Deep Neural Network Mapping
Srivatsan Krishnan, Jason Jabbour, Dan Zhang +4
Mapping deep neural networks (DNNs) to hardware is critical for optimizing latency, energy consumption, and resource utilization, making it a cornerstone of high-performance accele…
Enter the Mind Palace: Reasoning and Planning for Long-term Active Embodied Question Answering
Muhammad Fadhil Ginting, Dong-Ki Kim, Xiangyun Meng +10
As robots become increasingly capable of operating over extended periods -- spanning days, weeks, and even months -- they are expected to accumulate knowledge of their environments…
Adaptive Accompaniment with ReaLchords
Yusong Wu, Tim Cooijmans, Kyle Kastner +10
Jamming requires coordination, anticipation, and collaborative creativity between musicians. Current generative models of music produce expressive output but are not able to genera…
FALCON: Learning Force-Adaptive Humanoid Loco-Manipulation
Yuanhang Zhang, Yifu Yuan, Prajwal Gurunath +7
Humanoid loco-manipulation holds transformative potential for daily service and industrial tasks, yet achieving precise, robust whole-body control with 3D end-effector force intera…
SayComply: Grounding Field Robotic Tasks in Operational Compliance through Retrieval-Based Language Models
Muhammad Fadhil Ginting, Dong-Ki Kim, Sung-Kyun Kim +4
This paper addresses the problem of task planning for robots that must comply with operational manuals in real-world settings. Task planning under these constraints is essential fo…
Beyond Thumbs Up/Down: Untangling Challenges of Fine-Grained Feedback for Text-to-Image Generation
Katherine M. Collins, Najoung Kim, Yonatan Bitton +15
Human feedback plays a critical role in learning and refining reward models for text-to-image generation, but the optimal form the feedback should take for learning an accurate rew…
Collision Avoidance Verification of Multiagent Systems with Learned Policies
Zihao Dong, Shayegan Omidshafiei, Michael Everett
For many multiagent control problems, neural networks (NNs) have enabled promising new capabilities. However, many of these systems lack formal guarantees (e.g., collision avoidanc…
Deconstructing Cooperation and Ostracism via Multi-Agent Reinforcement Learning
Atsushi Ueshima, Shayegan Omidshafiei, Hirokazu Shirado
Cooperation is challenging in biological systems, human societies, and multi-agent systems in general. While a group can benefit when everyone cooperates, it is tempting for each a…
DRIP: Domain Refinement Iteration with Polytopes for Backward Reachability Analysis of Neural Feedback Loops
Michael Everett, Rudy Bunel, Shayegan Omidshafiei
Safety certification of data-driven control techniques remains a major open problem. This work investigates backward reachability as a framework for providing collision avoidance g…
Multi-Agent Reinforcement Learning for Microprocessor Design Space Exploration
Srivatsan Krishnan, Natasha Jaques, Shayegan Omidshafiei +4
Microprocessor architects are increasingly resorting to domain-specific customization in the quest for high-performance and energy-efficiency. As the systems grow in complexity, fi…
Game Theoretic Rating in N-player general-sum games with Equilibria
Luke Marris, Marc Lanctot, Ian Gemp +5
Rating strategies in a game is an important area of research in game theory and artificial intelligence, and can be applied to any real-world competitive or cooperative setting. Tr…
Mastering the Game of Stratego with Model-Free Multiagent Reinforcement Learning
Julien Perolat, Bart de Vylder, Daniel Hennes +31
We introduce DeepNash, an autonomous agent capable of learning to play the imperfect information game Stratego from scratch, up to a human expert level. Stratego is one of the few…