STOMP: Stochastic trajectory optimization for motion planning

Kalakrishnan, Mrinal; Chitta, Sachin; Theodorou, Evangelos A.; Pástor, Peter; Schaal, Stefan

doi:10.1109/icra.2011.5980280

Cited by 742 publications

(551 citation statements)

References 15 publications

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“…Our first contribution is to generate legible motion via functional gradient optimization in the space of trajectories (Sec. IV), echoing earlier works in motion planning [9,21,22,27,29,33,35], now with legibility as an optimization criterion. Fig.1 depicts this optimization process: by exaggerating the motion to the right, the robot makes the other goal option, G O , far less likely to be inferred by the observer that the correct goal G R .…”

Section: Introductionmentioning

confidence: 87%

Generating Legible Motion

Dragan¹,

Srinivasa²

2013

Robotics: Science and Systems IX

110

119

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Abstract-Legible motion -motion that communicates its intent to a human observer -is crucial for enabling seamless human-robot collaboration. In this paper, we propose a functional gradient optimization technique for autonomously generating legible motion. Our algorithm optimizes a legibility metric inspired by the psychology of action interpretation in humans, resulting in motion trajectories that purposefully deviate from what an observer would expect in order to better convey intent. A trust region constraint on the optimization ensures that the motion does not become too surprising or unpredictable to the observer.Our studies with novice users that evaluate the resulting trajectories support the applicability of our method and of such a trust region. They show that within the region, legibility as measured in practice does significantly increase. Outside of it, however, the trajectory becomes confusing and the users' confidence in knowing the robot's intent significantly decreases.

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Section: Introductionmentioning

confidence: 87%

Generating Legible Motion

Dragan¹,

Srinivasa²

2013

Robotics: Science and Systems IX

110

119

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“…While efficient trajectory optimization techniques do exist for high-dimensional spaces and non-convex costs [30], they are subject to local minima, and how to alleviate this issue in practice remains an open research question [31], [32]. Fig.…”

Section: B Evaluating and Generating Predictabilitymentioning

confidence: 99%

“…", to "I know how to make you believe I am grasping this". We do so via functional gradient optimization in the space of motion trajectories, echoing earlier works in motion planning [9,28,31,43,45,53,55,61], now with optimization criteria based on the observer's inferences.…”

mentioning

confidence: 99%

Integrating human observer inferences into robot motion planning

Dragan

Srinivasa

2014

Auton Robot

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Abstract-Our goal is to enable robots to produce motion that is suitable for human-robot collaboration and coexistence. Most motion in robotics is purely functional, ideal when the robot is performing a task in isolation. In collaboration, however, the robot's motion has an observer, watching and interpreting the motion.In this work, we move beyond functional motion, and introduce the notion of an observer into motion planning, so that robots can generate motion that is mindful of how it will be interpreted by a human collaborator.We formalize predictability and legibility as properties of motion that naturally arise from the inferences in opposing directions that the observer makes, drawing on action interpretation theory in psychology. We propose models for these inferences based on the principle of rational action, and derive constrained functional trajectory optimization techniques for planning motion that is predictable or legible.Finally, we present experiments that test our work on novice users, and discuss the remaining challenges in enabling robots to generate such motion online in complex situations. 1

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“…While trajectory optimization and recent methods for motion planning based on trajectory optimization (Ratliff, Zucker, Bagnell, & Srinivasa, 2009;Kalakrishnan et al, 2011) have been widely accepted in robotics, their focus is to optimally achieve a goal while satisfying task constraints such as obstacle avoidance and joint/torque limits. Different from imitation learning based methods, trajectory optimization does not exploit a prior of human demonstrations and does not address action recognition.…”

Section: Interaction Promps and Trajectory Optimizationmentioning

confidence: 99%

“…In the cited work, the intrinsic correlation of the movements of different agents are not exploited while here, the inference of the robot trajectory and the recognition of the human action are parametrically correlated. As a result, in the work of Mainprice & Berenson, an independent motion planning procedure-using STOMP (Kalakrishnan et al, 2011)-had to be used specifically to generate the robot trajectories once the intent of the human was recognized.…”

Section: Hybrid Approaches For Action Recognition and Robot Controlmentioning

confidence: 99%

Probabilistic movement primitives for coordination of multiple human–robot collaborative tasks

et al. 2016

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This paper proposes an interaction learning method for collaborative and assistive robots based on movement primitives. The method allows for both action recognition and human-robot movement coordination. It uses imitation learning to construct a mixture model of human-robot interaction primitives. This probabilistic model allows the assistive trajectory of the robot to be inferred from human observations. The method is scalable in relation to the number of tasks and can learn nonlinear correlations between the trajectories that describe the human-robot interaction. We evaluated the method experimentally with a lightweight robot arm in a variety of assistive scenarios, including the coordinated handover of a bottle to a human, and the collaborative assembly of a toolbox. Potential applications of the method are personal caregiver robots, control of intelligent prosthetic devices, and robot coworkers in factories.Keywords Movement primitives · physical humanrobot interaction · imitation learning · mixture model · action recognition · trajectory generation

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STOMP: Stochastic trajectory optimization for motion planning

Cited by 742 publications

References 15 publications

Generating Legible Motion

Generating Legible Motion

Integrating human observer inferences into robot motion planning

Probabilistic movement primitives for coordination of multiple human–robot collaborative tasks

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