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Language-Conditioned Imitation Learning for Robot Manipulation Tasks

Imitation learning is a popular approach for teaching motor skills to robots. However, most approaches focus on extracting policy parameters from execution traces alone (i.e., motion trajectories and perceptual data). No adequate communication channel exists between the human expert and the robot to describe critical aspects of the task, such as the properties of the…

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Improved Exploration Through Latent Trajectory Optimization in Deep Deterministic Policy Gradient

Model-free reinforcement learning algorithms such as Deep Deterministic Policy Gradient (DDPG) often require additional exploration strategies, especially if the actor is of deterministic nature. 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. In addition, an extension of DDPG…

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Learning Interactive Behaviors for Musculoskeletal Robots Using Bayesian Interaction Primitives

nMusculoskeletal 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. However, programming interactive and responsive behaviors for such systems is extremely challenging due to the nonlinearity and uncertainty inherent to their control. In this paper, we propose an approach…

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Extrinsic Dexterity Through Active Slip Control Using Deep Predictive Models

We present a machine learning methodology for actively controlling slip, in order to increase robot dexterity. Leveraging recent insights in deep learning, we propose a Deep Predictive Model that uses tactile sensor information to reason about slip and its future influence on the manipulated object. The obtained information is then used to precisely manipulate objects…

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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. Despite this observation, few HRI methods are capable of efficiently performing inference for multimodal systems. In this work, we introduce a reformulation of Interaction Primitives which allows for learning from demonstration of interaction tasks, while also gracefully handling nonlinearities…

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A System for Learning Continuous Human-Robot Interactions from Human-Human Demonstrations

We present a data-driven imitation learning system for learning human-robot interactions from human-human demonstrations. During training, the movements of two interaction partners are recorded through motion capture and an interaction model is learned. At runtime, the interaction model is used to continuously adapt the robot’s motion, both spatially and temporally, to the movements of the…

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Learning Human-Robot Interactions from Human-Human Demonstrations (With Applications in Lego Rocket Assembly)

This video demonstrates a novel imitation learning approach for learning human-robot interactions from human- human demonstrations. During training, the movements of two human interaction partners are recorded via motion capture. From this, an interaction model is learned that inherently captures important spatial relationships as well as temporal synchrony of body movements between the two interacting…

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