Search Results for author:
Found 19 papers, 2 papers with code
"Task Success" is not Enough: Investigating the Use of Video-Language Models as Behavior Critics for Catching Undesirable Agent Behaviors
Large-scale generative models are shown to be useful for sampling meaningful candidate solutions, yet they often overlook task constraints and user preferences.
Learning and Blending Robot Hugging Behaviors in Time and Space
We introduce an imitation learning-based physical human-robot interaction algorithm capable of predicting appropriate robot responses in complex interactions involving a superposition of multiple interactions.
Local Repair of Neural Networks Using Optimization
In this paper, we propose a framework to repair a pre-trained feed-forward neural network (NN) to satisfy a set of properties.
Learning Predictive Models for Ergonomic Control of Prosthetic Devices
We present Model-Predictive Interaction Primitives -- a robot learning framework for assistive motion in human-machine collaboration tasks which explicitly accounts for biomechanical impact on the human musculoskeletal system.
Language-Conditioned Imitation Learning for Robot Manipulation Tasks
Imitation learning is a popular approach for teaching motor skills to robots.
Predictive Modeling of Periodic Behavior for Human-Robot Symbiotic Walking
We propose in this paper Periodic Interaction Primitives - a probabilistic framework that can be used to learn compact models of periodic behavior.
DeepCrashTest: Turning Dashcam Videos into Virtual Crash Tests for Automated Driving Systems
The goal of this paper is to generate simulations with real-world collision scenarios for training and testing autonomous vehicles.
Assistive Relative Pose Estimation for On-orbit Assembly using Convolutional Neural Networks
Accurate real-time pose estimation of spacecraft or object in space is a key capability necessary for on-orbit spacecraft servicing and assembly tasks.
Clone Swarms: Learning to Predict and Control Multi-Robot Systems by Imitation
In this paper, we propose SwarmNet -- a neural network architecture that can learn to predict and imitate the behavior of an observed swarm of agents in a centralized manner.
Imitation Learning of Robot Policies by Combining Language, Vision and Demonstration
In this work we propose a novel end-to-end imitation learning approach which combines natural language, vision, and motion information to produce an abstract representation of a task, which in turn is used to synthesize specific motion controllers at run-time.
Data-efficient Co-Adaptation of Morphology and Behaviour with Deep Reinforcement Learning
Key to our approach is the possibility of leveraging previously tested morphologies and behaviors to estimate the performance of new candidate morphologies.
Improved Exploration through Latent Trajectory Optimization in Deep Deterministic Policy Gradient
This work evaluates the use of model-based trajectory optimization methods used for exploration in Deep Deterministic Policy Gradient when trained on a latent image embedding.
Towards Scalable Imitation Learning for Multi-Agent Systems with Graph Neural Networks
We propose an implementation of GNN that predicts and imitates the motion be- haviors from observed swarm trajectory data.
Imitation Learning of Robot Policies using Language, Vision and Motion
In this work we propose a novel end-to-end imitation learning approach which combines natural language, vision, and motion information to produce an abstract representation of a task, which in turn can be used to synthesize specific motion controllers at run-time.
Multimodal Dataset of Human-Robot Hugging Interaction
33 people were given minimal instructions to hug the humanoid robot for as natural hugging interaction as possible.
Learning Interactive Behaviors for Musculoskeletal Robots Using Bayesian Interaction Primitives
Musculoskeletal robots that are based on pneumatic actuation have a variety of properties, such as compliance and back-drivability, that render them particularly appealing for human-robot collaboration.
Probabilistic Multimodal Modeling for Human-Robot Interaction Tasks
Human-robot interaction benefits greatly from multimodal sensor inputs as they enable increased robustness and generalization accuracy.
Information Maximizing Exploration with a Latent Dynamics Model
A key insight of our approach is that this dynamics model can be learned in the latent feature space of a value function, representing the dynamics of the agent and the environment.
Occlusion-Aware Object Localization, Segmentation and Pose Estimation
We present a learning approach for localization and segmentation of objects in an image in a manner that is robust to partial occlusion.
