Guiding Trajectory Optimization by Demonstrated Distributions

Osa, Takayuki; Esfahani, Amir Masoud Ghalamzan; Stolkin, Rustam; Lioutikov, Rudolf; Peters, Jan; Neumann, Gerhard

doi:10.1109/lra.2017.2653850

Cited by 46 publications

(40 citation statements)

References 20 publications

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“…Gaussian Model Learning GMR [Calinon and Billard, 2009, Gribovskaya et al, 2011, Khansari-Zadeh and Billard, 2014, Calinon, 2016 LWR [Schaal and Atkeson, 1998, Mülling et al, 2013, Osa et al, 2017a] LWPR [Vijayakumar et al, 2005] GPR [Osa et al, 2017b] Action-State…”

Section: Trajectorymentioning

confidence: 99%

“…The approach based on TP-GMM has been recently employed in several studies [Calinon, 2016, Rozo et al, 2016. The recent work by Osa et al [2017a] proposed a trajectory optimization method for collision avoidance, which incorporates the distribution of the demonstrated trajectories. In ad- Table 3.5: Generalization of skills using existing methods.…”

Section: Motion Generalization With Dmpmentioning

confidence: 99%

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An Algorithmic Perspective on Imitation Learning

et al. 2018

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As robots and other intelligent agents move from simple environments and problems to more complex, unstructured settings, manually programming their behavior has become increasingly challenging and expensive. Often, it is easier for a teacher to demonstrate a desired behavior rather than attempt to manually engineer it. This process of learning from demonstrations, and the study of algorithms to do so, is called imitation learning. This work provides an introduction to imitation learning. It covers the underlying assumptions, approaches, and how they relate; the rich set of algorithms developed to tackle the problem; and advice on effective tools and implementation.We intend this paper to serve two audiences. First, we want to familiarize machine learning experts with the challenges of imitation learning, particularly those arising in robotics, and the interesting theoretical and practical distinctions between it and more familiar frameworks like statistical supervised learning theory and reinforcement learning. Second, we want to give roboticists and experts in applied artificial intelligence a broader appreciation for the frameworks and tools available for imitation learning.We organize our work by dividing imitation learning into directly replicating desired behavior (sometimes called behavioral cloning [Bain and Sammut, 1996]) and learning the hidden objectives of the desired behavior from demonstrations (called inverse optimal control [Kalman, 1964] or inverse reinforcement learning [Russell, 1998]). In addition to method analysis, we discuss the design decisions a practitioner must make when selecting an imitation learning approach. Moreover, application examples-such as robots that play table tennis [Kober and Peters, 2009] and programs that play the game of Go [Silver et al., 2016]-illustrate the properties and motivations behind different forms of imitation learning. We conclude by presenting a set of open questions and point towards possible future research directions.

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Section: Trajectorymentioning

confidence: 99%

Section: Motion Generalization With Dmpmentioning

confidence: 99%

An Algorithmic Perspective on Imitation Learning

et al. 2018

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“…The motion planning framework in [15], complementary to our approach, utilizes a blended cost function, the construction of which is guided by probability distributions learned from the demonstrations. This framework incentivizes factors such as smoothness, manipulability, and obstacle avoidance, but is restricted to the Cartesian coordinate system.…”

Section: Related Workmentioning

confidence: 99%

Skill Acquisition via Automated Multi-Coordinate Cost Balancing

Ravichandar

Ahmadzadeh

Rana

et al. 2019

2019 International Conference on Robotics and Automation (ICRA)

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We propose a learning framework, named Multi-Coordinate Cost Balancing (MCCB), to address the problem of acquiring point-to-point movement skills from demonstrations. MCCB encodes demonstrations simultaneously in multiple differential coordinates that specify local geometric properties. MCCB generates reproductions by solving a convex optimization problem with a multi-coordinate cost function and linear constraints on the reproductions, such as initial, target, and via points. Further, since the relative importance of each coordinate system in the cost function might be unknown for a given skill, MCCB learns optimal weighting factors that balance the cost function. We demonstrate the effectiveness of MCCB via detailed experiments conducted on one handwriting dataset and three complex skill datasets.

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“…1, in which the robot is tasked with grasping the book and then moving it to a desired destination. Methods for reaching the object and grasping it may include predicting stable grasps from visual information [1], while methods for planning the post-grasp trajectory might include learning trajectories from demonstration [2] or optimising a trajectory using numerical methods [3].…”

Section: Introductionmentioning

confidence: 99%

“…(2). While the analytical dynamics may not be known exactly in real applications, it is reasonable to assume that an approximation of the dynamic model can be identified.…”

Section: Introductionmentioning

confidence: 99%

Safe robotic grasping: Minimum impact-force grasp selection

Mavrakis

Ghalamzan

2017

2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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Abstract-This paper addresses the problem of selecting from a choice of possible grasps, so that impact forces will be minimised if a collision occurs while the robot is moving the grasped object along a post-grasp trajectory. Such considerations are important for safety in human-robot interaction, where even a certified "human-safe" (e.g. compliant) arm may become hazardous once it grasps and begins moving an object, which may have significant mass, sharp edges or other dangers. Additionally, minimising collision forces is critical to preserving longevity of robots which operate in uncertain and hazardous environments, e.g. robots deployed for nuclear decommissioning, where removing a damaged robot from a contaminated zone for repairs may be extremely difficult and costly. Also, unwanted collisions between a robot and critical infrastructure (e.g. pipework) in such high-consequence environments can be disastrous. In this paper we investigate how the safety of the post-grasp motion can be considered during the pre-grasp approach phase, so that the selected grasp is optimal in terms applying minimum impact forces if a collision occurs during a desired post-grasp manipulation. We build on the methods of augmented robot-object dynamics models and "effective mass" and propose a method for combining these concepts with modern grasp and trajectory planners, to enable the robot to achieve a grasp which maximises the safety of the postgrasp trajectory, by minimising potential collision forces. We demonstrate the effectiveness of our approach through several experiments with both simulated and real robots.

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Guiding Trajectory Optimization by Demonstrated Distributions

Cited by 46 publications

References 20 publications

An Algorithmic Perspective on Imitation Learning

An Algorithmic Perspective on Imitation Learning

Skill Acquisition via Automated Multi-Coordinate Cost Balancing

Safe robotic grasping: Minimum impact-force grasp selection

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