Search Results for author:
Found 13 papers, 4 papers with code
Mobility VLA: Multimodal Instruction Navigation with Long-Context VLMs and Topological Graphs
The high-level policy consists of a long-context VLM that takes the demonstration tour video and the multimodal user instruction as input to find the goal frame in the tour video.
Modeling the Real World with High-Density Visual Particle Dynamics
We present High-Density Visual Particle Dynamics (HD-VPD), a learned world model that can emulate the physical dynamics of real scenes by processing massive latent point clouds containing 100K+ particles.
Revisiting Energy Based Models as Policies: Ranking Noise Contrastive Estimation and Interpolating Energy Models
In this work, we revisit the choice of energy-based models (EBM) as a policy class.
Robots That Ask For Help: Uncertainty Alignment for Large Language Model Planners
Large language models (LLMs) exhibit a wide range of promising capabilities -- from step-by-step planning to commonsense reasoning -- that may provide utility for robots, but remain prone to confidently hallucinated predictions.
RoboPianist: Dexterous Piano Playing with Deep Reinforcement Learning
Replicating human-like dexterity in robot hands represents one of the largest open problems in robotics.
Robotic Table Wiping via Reinforcement Learning and Whole-body Trajectory Optimization
This problem is challenging, as it requires planning wiping actions while reasoning over uncertain latent dynamics of crumbs and spills captured via high-dimensional visual observations.
Learning Model Predictive Controllers with Real-Time Attention for Real-World Navigation
Despite decades of research, existing navigation systems still face real-world challenges when deployed in the wild, e. g., in cluttered home environments or in human-occupied public spaces.
Multiscale Sensor Fusion and Continuous Control with Neural CDEs
Though robot learning is often formulated in terms of discrete-time Markov decision processes (MDPs), physical robots require near-continuous multiscale feedback control.
Learning Stabilizable Nonlinear Dynamics with Contraction-Based Regularization
We propose a novel framework for learning stabilizable nonlinear dynamical systems for continuous control tasks in robotics.
Improving Robustness of Machine Translation with Synthetic Noise
Modern Machine Translation (MT) systems perform consistently well on clean, in-domain text.
Learning Stabilizable Dynamical Systems via Control Contraction Metrics
We propose a novel framework for learning stabilizable nonlinear dynamical systems for continuous control tasks in robotics.
Language Informed Modeling of Code-Switched Text
Code-switching (CS), the practice of alternating between two or more languages in conversations, is pervasive in most multi-lingual communities.
Risk-sensitive Inverse Reinforcement Learning via Semi- and Non-Parametric Methods
The literature on Inverse Reinforcement Learning (IRL) typically assumes that humans take actions in order to minimize the expected value of a cost function, i. e., that humans are risk neutral.
