5,380 research outputs found
Data-Driven Grasp Synthesis - A Survey
We review the work on data-driven grasp synthesis and the methodologies for
sampling and ranking candidate grasps. We divide the approaches into three
groups based on whether they synthesize grasps for known, familiar or unknown
objects. This structure allows us to identify common object representations and
perceptual processes that facilitate the employed data-driven grasp synthesis
technique. In the case of known objects, we concentrate on the approaches that
are based on object recognition and pose estimation. In the case of familiar
objects, the techniques use some form of a similarity matching to a set of
previously encountered objects. Finally for the approaches dealing with unknown
objects, the core part is the extraction of specific features that are
indicative of good grasps. Our survey provides an overview of the different
methodologies and discusses open problems in the area of robot grasping. We
also draw a parallel to the classical approaches that rely on analytic
formulations.Comment: 20 pages, 30 Figures, submitted to IEEE Transactions on Robotic
Learning multi-stage tasks with one demonstration via self-replay
In this work, we introduce a novel method to learn everyday-like multistage tasks from a single human demonstration, without requiring any prior object knowledge. Inspired by the recent Coarse-to-Fine Imitation Learning method, we model imitation learning as a learned object reaching phase followed by an openloop replay of the demonstrator’s actions. We build upon this for multi-stage tasks where, following the human demonstration, the robot can autonomously collect image data for the entire multi-stage task, by reaching the next object in the sequence and then replaying the demonstration, and then repeating in a loop for all stages of the task. We evaluate with real-world experiments on a set of everydaylike multi-stage tasks, which we show that our method can solve from a single demonstration. Videos and supplementary material can be found at this webpage
SHAIL: Safety-Aware Hierarchical Adversarial Imitation Learning for Autonomous Driving in Urban Environments
Designing a safe and human-like decision-making system for an autonomous
vehicle is a challenging task. Generative imitation learning is one possible
approach for automating policy-building by leveraging both real-world and
simulated decisions. Previous work that applies generative imitation learning
to autonomous driving policies focuses on learning a low-level controller for
simple settings. However, to scale to complex settings, many autonomous driving
systems combine fixed, safe, optimization-based low-level controllers with
high-level decision-making logic that selects the appropriate task and
associated controller. In this paper, we attempt to bridge this gap in
complexity by employing Safety-Aware Hierarchical Adversarial Imitation
Learning (SHAIL), a method for learning a high-level policy that selects from a
set of low-level controller instances in a way that imitates low-level driving
data on-policy. We introduce an urban roundabout simulator that controls
non-ego vehicles using real data from the Interaction dataset. We then show
empirically that our approach can produce better behavior than previous
approaches in driver imitation which have difficulty scaling to complex
environments. Our implementation is available at
https://github.com/sisl/InteractionImitation
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