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Found 26 papers, 10 papers with code
Causal Composition Diffusion Model for Closed-loop Traffic Generation
Then, CCDiff maximizes controllability while adhering to realism by automatically identifying and injecting causal structures directly into the diffusion process, providing structured guidance to enhance both realism and controllability.
DriveGPT: Scaling Autoregressive Behavior Models for Driving
We present DriveGPT, a scalable behavior model for autonomous driving.
VLM-AD: End-to-End Autonomous Driving through Vision-Language Model Supervision
Human drivers rely on commonsense reasoning to navigate diverse and dynamic real-world scenarios.
NEWTON: Are Large Language Models Capable of Physical Reasoning?
To address this gap, we introduce NEWTON, a repository and benchmark for evaluating the physics reasoning skills of LLMs.
Real World Offline Reinforcement Learning with Realistic Data Source
Offline reinforcement learning (ORL) holds great promise for robot learning due to its ability to learn from arbitrary pre-generated experience.
Git Re-Basin: Merging Models modulo Permutation Symmetries
The success of deep learning is due in large part to our ability to solve certain massive non-convex optimization problems with relative ease.
Iterative Linear Quadratic Optimization for Nonlinear Control: Differentiable Programming Algorithmic Templates
We present the implementation of nonlinear control algorithms based on linear and quadratic approximations of the objective from a functional viewpoint.
Leveraging Experience in Lazy Search
If new search problems are sufficiently similar to problems solved during training, the learned policy will choose a good edge evaluation ordering and solve the motion planning problem quickly.
Faster Policy Learning with Continuous-Time Gradients
We study the estimation of policy gradients for continuous-time systems with known dynamics.
Grasping with Chopsticks: Combating Covariate Shift in Model-free Imitation Learning for Fine Manipulation
Billions of people use chopsticks, a simple yet versatile tool, for fine manipulation of everyday objects.
Amodal 3D Reconstruction for Robotic Manipulation via Stability and Connectivity
By using these priors over the physical properties of objects, our system improves reconstruction quality not just by standard visual metrics, but also performance of model-based control on a variety of robotics manipulation tasks in challenging, cluttered environments.
Lyceum: An efficient and scalable ecosystem for robot learning
We introduce Lyceum, a high-performance computational ecosystem for robot learning.
Mo' States Mo' Problems: Emergency Stop Mechanisms from Observation
In many environments, only a relatively small subset of the complete state space is necessary in order to accomplish a given task.
Leveraging Experience in Lazy Search
If new search problems are sufficiently similar to problems solved during training, the learned policy will choose a good edge evaluation ordering and solve the motion planning problem quickly.
Imitation Learning as $f$-Divergence Minimization
We show that the state-of-the-art methods such as GAIL and behavior cloning, due to their choice of loss function, often incorrectly interpolate between such modes.
Improving Robot Success Detection using Static Object Data
We collect over 13 hours of egocentric manipulation data for training a model to reason about whether a robot successfully placed unseen objects in or on one another.
Tactical Rewind: Self-Correction via Backtracking in Vision-and-Language Navigation
We present the Frontier Aware Search with backTracking (FAST) Navigator, a general framework for action decoding, that achieves state-of-the-art results on the Room-to-Room (R2R) Vision-and-Language navigation challenge of Anderson et.
Ranked #4 on
Vision-Language Navigation
on Room2Room
The Assistive Multi-Armed Bandit
Learning preferences implicit in the choices humans make is a well studied problem in both economics and computer science.
Learning Configuration Space Belief Model from Collision Checks for Motion Planning
Our aim is to reduce the expected number of collision checks by creating a belief model of the configuration space using results from collision tests.
Sample-Efficient Learning of Nonprehensile Manipulation Policies via Physics-Based Informed State Distributions
This paper proposes a sample-efficient yet simple approach to learning closed-loop policies for nonprehensile manipulation.
Balancing Shared Autonomy with Human-Robot Communication
Robotic agents that share autonomy with a human should leverage human domain knowledge and account for their preferences when completing a task.
Recurrent Predictive State Policy Networks
Predictive state policy networks consist of a recursive filter, which keeps track of a belief about the state of the environment, and a reactive policy that directly maps beliefs to actions, to maximize the cumulative reward.
Trust-Aware Decision Making for Human-Robot Collaboration: Model Learning and Planning
The trust-POMDP model provides a principled approach for the robot to (i) infer the trust of a human teammate through interaction, (ii) reason about the effect of its own actions on human trust, and (iii) choose actions that maximize team performance over the long term.
Bayesian Active Edge Evaluation on Expensive Graphs
We are interested in planning algorithms that actively infer the underlying structure of the valid configuration space during planning in order to find solutions with minimal effort.
Near-Optimal Edge Evaluation in Explicit Generalized Binomial Graphs
By leveraging this property, we are able to significantly reduce computational complexity from exponential to linear in the number of edges.
Robotics
Near Optimal Bayesian Active Learning for Decision Making
Instead of minimizing uncertainty per se, we consider a set of overlapping decision regions of these hypotheses.
