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Found 16 papers, 4 papers with code
Benchmarking Robot Manipulation with the Rubik's Cube
Benchmarks for robot manipulation are crucial to measuring progress in the field, yet there are few benchmarks that demonstrate critical manipulation skills, possess standardized metrics, and can be attempted by a wide array of robot platforms.
Desk Organization: Effect of Multimodal Inputs on Spatial Relational Learning
We examine the problem of desk organization: learning how humans spatially position different objects on a planar surface according to organizational ''preference''.
Learning Online from Corrective Feedback: A Meta-Algorithm for Robotics
A key challenge in Imitation Learning (IL) is that optimal state actions demonstrations are difficult for the teacher to provide.
Multimodal Trajectory Prediction via Topological Invariance for Navigation at Uncontrolled Intersections
Our key insight is that the geometric structure of the intersection and the incentive of agents to move efficiently and avoid collisions (rationality) reduces the space of likely behaviors, effectively relaxing the problem of trajectory prediction.
Leveraging Post Hoc Context for Faster Learning in Bandit Settings with Applications in Robot-Assisted Feeding
Our key insight is that we can leverage the haptic context we collect during and after manipulation (i. e., "post hoc") to learn some of these properties and more quickly adapt our visual model to previously unseen food.
Implicit Multiagent Coordination at Unsignalized Intersections via Multimodal Inference Enabled by Topological Braids
Often, the structure of these domains constrains multiagent trajectories to belong to a finite set of modes.
Bayesian Residual Policy Optimization: Scalable Bayesian Reinforcement Learning with Clairvoyant Experts
We first obtain an ensemble of experts, one for each latent MDP, and fuse their advice to compute a baseline policy.
Towards Effective Human-AI Teams: The Case of Collaborative Packing
Our goal is to guide human users towards optimal task performance while keeping their cognitive load as low as possible.
Adaptive Robot-Assisted Feeding: An Online Learning Framework for Acquiring Previously Unseen Food Items
A successful robot-assisted feeding system requires bite acquisition of a wide variety of food items.
Bayes-CPACE: PAC Optimal Exploration in Continuous Space Bayes-Adaptive Markov Decision Processes
We present the first PAC optimal algorithm for Bayes-Adaptive Markov Decision Processes (BAMDPs) in continuous state and action spaces, to the best of our knowledge.
Bayesian Policy Optimization for Model Uncertainty
Addressing uncertainty is critical for autonomous systems to robustly adapt to the real world.
Anytime Motion Planning on Large Dense Roadmaps with Expensive Edge Evaluations
We propose an algorithmic framework for efficient anytime motion planning on large dense geometric roadmaps, in domains where collision checks and therefore edge evaluations are computationally expensive.
Robotics
The Provable Virtue of Laziness in Motion Planning
The Lazy Shortest Path (LazySP) class consists of motion-planning algorithms that only evaluate edges along shortest paths between the source and target.
Robotics Data Structures and Algorithms
A Linear-Time Variational Integrator for Multibody Systems
Our key contribution is to derive a recursive algorithm that evaluates DEL equations in $O(n)$, which scales up well for complex multibody systems such as humanoid robots.
Robotics
Batch Informed Trees (BIT*): Sampling-based Optimal Planning via the Heuristically Guided Search of Implicit Random Geometric Graphs
In this paper, we present Batch Informed Trees (BIT*), a planning algorithm based on unifying graph- and sampling-based planning techniques.
Robotics
Informed RRT*: Optimal Sampling-based Path Planning Focused via Direct Sampling of an Admissible Ellipsoidal Heuristic
We present the algorithm as a simple modification to RRT* that could be further extended by more advanced path-planning algorithms.
Robotics
