Recent Advances in Robot Learning from Demonstration

Ravichandar, Harish; Polydoros, Athanasios; Chernova, Sonia; Billard, Aude

doi:10.1146/annurev-control-100819-063206

Cited by 447 publications

(255 citation statements)

References 201 publications

(323 reference statements)

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“…Some recent research directions present interesting relations with the techniques proposed here and are worth some discussions. Recent reviews provide additional background on learning objectives from demonstrations [10], [11].…”

Section: Qualitative Comparison With Related Workmentioning

confidence: 99%

“…However, these methods require the data to be segmented into specific maneuvers in which the assumptions on the data-expressed by the KKT conditionsare satisfied. Automatic segmentation of demonstrations into maneuvers or subtasks is addressed, e.g., in [25]- [27], and the reader is referred to [10] for a detailed discussion. In contrast, for our method, the data need not be segmented into maneuvers prior to learning.…”

Section: A Inverse Optimal Control (Ioc)mentioning

confidence: 99%

“…1) Gradient computation: The gradient of the prior belief log p(Q, R|Q c , R c ) in (14) depends on the functions t c and t s in (12). For computing the gradients of log p(x 0:T , y 0:T |Q, R) with respect to Q and R, we use (10),…”

Section: B Optimization Algorithm: Projected Gradient Descentmentioning

confidence: 99%

See 2 more Smart Citations

Inverse Learning for Data-Driven Calibration of Model-Based Statistical Path Planning

Menner

Berntorp

Zeilinger

et al. 2021

IEEE Trans. Intell. Veh.

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This paper presents a method for inverse learning of a control objective defined in terms of requirements and their joint probability distribution from data. The probability distribution characterizes tolerated deviations from the deterministic requirements and is learned using maximum likelihood estimation from data. Further, this paper introduces both parametrized requirements for motion planning in autonomous driving applications and methods for the estimation of their parameters from driving data. Both the parametrized requirements and their joint probability distributions are estimated using a posterior distribution such that the control objective is personalized from a prior as driver data are accumulated. Finally, three variants of the learning method are presented that vary in computational complexity and data storage requirements. Key advantages of the proposed inverse learning method are a relatively low computational complexity, a need for a limited amount of data, and that the data do not have to be segmented into specific maneuvers, which makes the method easily implementable. Learning results using data of five human drivers in a simulation environment suggest that the proposed model for human-conscious driving along with the proposed learning method enable a more natural and personalized driving style of autonomous vehicles for their human passengers.

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Section: Qualitative Comparison With Related Workmentioning

confidence: 99%

Section: A Inverse Optimal Control (Ioc)mentioning

confidence: 99%

See 1 more Smart Citation

Inverse Learning for Data-Driven Calibration of Model-Based Statistical Path Planning

Menner

Berntorp

Zeilinger

et al. 2021

IEEE Trans. Intell. Veh.

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“…Imitation Learning (IL) or Learning from Demonstration (LfD) methods are tools to give machines the ability to mimic human behavior to perform a task (Hussein et al, 2017;Ravichandar et al, 2020). In this vein, LfD is a userfriendly and intuitive methodology for non-roboticists to teach a new task to a robot.…”

Section: Vil Via Imitation Learningmentioning

confidence: 99%

“…Therefore, we focus on limitations of IL that particularly affect impedance learning. The interested reader is referred toRavichandar et al (2020) for a general discussion about strengths and limitations of IL.Frontiers in Robotics and AI | www.frontiersin.org…”

mentioning

confidence: 99%

Variable Impedance Control and Learning—A Review

Abu‐Dakka

Saveriano

2020

Front. Robot. AI

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Robots that physically interact with their surroundings, in order to accomplish some tasks or assist humans in their activities, require to exploit contact forces in a safe and proficient manner. Impedance control is considered as a prominent approach in robotics to avoid large impact forces while operating in unstructured environments. In such environments, the conditions under which the interaction occurs may significantly vary during the task execution. This demands robots to be endowed with online adaptation capabilities to cope with sudden and unexpected changes in the environment. In this context, variable impedance control arises as a powerful tool to modulate the robot's behavior in response to variations in its surroundings. In this survey, we present the state-of-the-art of approaches devoted to variable impedance control from control and learning perspectives (separately and jointly). Moreover, we propose a new taxonomy for mechanical impedance based on variability, learning, and control. The objective of this survey is to put together the concepts and efforts that have been done so far in this field, and to describe advantages and disadvantages of each approach. The survey concludes with open issues in the field and an envisioned framework that may potentially solve them.

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Predictive Learning of Error Recovery with a Sensorized Passivity‐Based Soft Anthropomorphic Hand

Gilday

Thuruthel

Iida

2023

Advanced Intelligent Systems

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Manipulation strategies based on the passive dynamics of soft-bodied interactions provide robust performances with limited sensory information. They utilize the kinematic structure and passive dynamics of the body to adapt to objects of varying shapes and properties. However, these soft passive interactions make the state of the robotic device influenced by the environment, making control generation and state estimation difficult. This work presents a closed-loop framework for dynamic interaction-based grasping that relies on two novelties: 1) a wrist-driven passive soft anthropomorphic hand that can generate robust grasp strategies using one-step kinaesthetic teaching and 2) a learning-based perception system that uses temporal data from sparse tactile sensors to predict and adapt to failures before it happens. With the anthropomorphic soft design and wrist-driven control, it is shown that controllers can be generated robust to novel objects and location uncertainty. With the learning-based high-level perception system and 32 sensing receptors, it is shown that failures can be predicted in advance, further improving the robustness of the entire system by more than doubling the grasping success rate. From over 1000 real-world grasping trials, both the control and perception framework are also seen to be transferable to novel objects and conditions. An interactive preprint version of the article can be found here:

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Recent Advances in Robot Learning from Demonstration

Cited by 447 publications

References 201 publications

Inverse Learning for Data-Driven Calibration of Model-Based Statistical Path Planning

Inverse Learning for Data-Driven Calibration of Model-Based Statistical Path Planning

Variable Impedance Control and Learning—A Review

Predictive Learning of Error Recovery with a Sensorized Passivity‐Based Soft Anthropomorphic Hand

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