Learning robot in-hand manipulation with tactile features

Hoof, Herke van; Hermans, Tucker; Neumann, Gerhard; Peters, Jan

doi:10.1109/humanoids.2015.7363524

Cited by 132 publications

(87 citation statements)

References 36 publications

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“…Many of them are concerned with estimating the stability of a grasp before lifting an object [6,14], even suggesting a regrasp [60]. Only a few approaches learn entire manipulation policies through reinforcement only given haptic feedback [29,30,[61][62][63]65]. While [30] relies on raw force-torque feedback, [29,61,62] learn a low-dimensional representation of high-dimensional tactile data before learning a policy, and [63] learns a dynamics model of the tactile feedback in a latent space.…”

Section: A Contact-rich Manipulationmentioning

confidence: 99%

Making Sense of Vision and Touch: Learning Multimodal Representations for Contact-Rich Tasks

et al. 2020

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Contact-rich manipulation tasks in unstructured environments often require both haptic and visual feedback. It is non-trivial to manually design a robot controller that combines these modalities which have very different characteristics. While deep reinforcement learning has shown success in learning control policies for high-dimensional inputs, these algorithms are generally intractable to deploy on real robots due to sample complexity. In this work, we use self-supervision to learn a compact and multimodal representation of our sensory inputs, which can then be used to improve the sample efficiency of our policy learning. Evaluating our method on a peg insertion task, we show that it generalizes over varying geometries, configurations, and clearances, while being robust to external perturbations. We also systematically study different self-supervised learning objectives and representation learning architectures. Results are presented in simulation and on a physical robot.

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Section: A Contact-rich Manipulationmentioning

confidence: 99%

Making Sense of Vision and Touch: Learning Multimodal Representations for Contact-Rich Tasks

et al. 2020

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“…Reinforcement learning has been applied to a wide variety of robotic manipulation tasks, including grasping objects [19], in-hand object manipulation [30,38,32,23], manipulating fluids [35], door opening [44,3], and cloth folding [28]. However, applications of RL in the real world require considerable effort to design and evaluate the reward function.…”

Section: Related Workmentioning

confidence: 99%

End-To-End Robotic Reinforcement Learning without Reward Engineering

Singh¹,

Yang²,

Hartikainen³

et al. 2019

Robotics: Science and Systems XV

165

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The combination of deep neural network models and reinforcement learning algorithms can make it possible to learn policies for robotic behaviors that directly read in raw sensory inputs, such as camera images, effectively subsuming both estimation and control into one model. However, realworld applications of reinforcement learning must specify the goal of the task by means of a manually programmed reward function, which in practice requires either designing the very same perception pipeline that end-to-end reinforcement learning promises to avoid, or else instrumenting the environment with additional sensors to determine if the task has been performed successfully. In this paper, we propose an approach for removing the need for manual engineering of reward specifications by enabling a robot to learn from a modest number of examples of successful outcomes, followed by actively solicited queries, where the robot shows the user a state and asks for a label to determine whether that state represents successful completion of the task. While requesting labels for every single state would amount to asking the user to manually provide the reward signal, our method requires labels for only a tiny fraction of the states seen during training, making it an efficient and practical approach for learning skills without manually engineered rewards. We evaluate our method on real-world robotic manipulation tasks where the observations consist of images viewed by the robot's camera. In our experiments, our method effectively learns to arrange objects, place books, and drape cloth, directly from images and without any manually specified reward functions, and with only 1-4 hours of interaction with the real world. Videos of learned behavior are available at sites.

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“…Tactile servoing [15] has been applied to object manipulation on an industrial robot arm [16] and particle filter methods for controlling how to push objects using tactile feedback [17]. Bayesian methods have been proposed for in-hand manipulation [18], [19]; here we examine tactile manipulation from the perspective of biomimetic active perception.…”

Section: Background and Related Workmentioning

confidence: 99%