4,965 research outputs found
NOD-TAMP: Multi-Step Manipulation Planning with Neural Object Descriptors
Developing intelligent robots for complex manipulation tasks in household and
factory settings remains challenging due to long-horizon tasks, contact-rich
manipulation, and the need to generalize across a wide variety of object shapes
and scene layouts. While Task and Motion Planning (TAMP) offers a promising
solution, its assumptions such as kinodynamic models limit applicability in
novel contexts. Neural object descriptors (NODs) have shown promise in object
and scene generalization but face limitations in addressing broader tasks. Our
proposed TAMP-based framework, NOD-TAMP, extracts short manipulation
trajectories from a handful of human demonstrations, adapts these trajectories
using NOD features, and composes them to solve broad long-horizon tasks.
Validated in a simulation environment, NOD-TAMP effectively tackles varied
challenges and outperforms existing methods, establishing a cohesive framework
for manipulation planning. For videos and other supplemental material, see the
project website: https://sites.google.com/view/nod-tamp/
Agile Autonomous Driving using End-to-End Deep Imitation Learning
We present an end-to-end imitation learning system for agile, off-road
autonomous driving using only low-cost sensors. By imitating a model predictive
controller equipped with advanced sensors, we train a deep neural network
control policy to map raw, high-dimensional observations to continuous steering
and throttle commands. Compared with recent approaches to similar tasks, our
method requires neither state estimation nor on-the-fly planning to navigate
the vehicle. Our approach relies on, and experimentally validates, recent
imitation learning theory. Empirically, we show that policies trained with
online imitation learning overcome well-known challenges related to covariate
shift and generalize better than policies trained with batch imitation
learning. Built on these insights, our autonomous driving system demonstrates
successful high-speed off-road driving, matching the state-of-the-art
performance.Comment: 13 pages, Robotics: Science and Systems (RSS) 201
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