Recent advances in on-policy reinforcement learning (RL) methods enabled learning agents in virtual environments to master complex tasks with high-dimensional and continuous observation and action spaces. However, leveraging this family of algorithms in multi-fingered robotic grasping remains a challenge due to large sim-to-real fidelity gaps and the high sample complexity of on-policy RL algorithms. This work aims to bridge these gaps by first reinforcement-learning a multi-fingered robotic grasping policy in simulation that operates in the pixel space of the input: a single depth image. Using a mapping from pixel space to Cartesian space according to the depth map, this method transfers to the real world with high fidelity and introduces a novel attention mechanism that substantially improves grasp success rate in cluttered environments. Finally, the direct-generative nature of this method allows learning of multi-fingered grasps that have flexible end-effector positions, orientations and rotations, as well as all degrees of freedom of the hand.
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Generative Attention Learning: a “GenerAL” framework for high-performance multi-fingered grasping in clutter
Generative Attention Learning (GenerAL) is a framework for high-DOF multi-fingered grasping that is not only robust to
dense clutter and novel objects but also effective with a variety of different parallel-jaw and multi-fingered robot hands. This
framework introduces a novel attention mechanism that substantially improves the grasp success rate in clutter. Its generative
nature allows the learning of full-DOF grasps with flexible end-effector positions and orientations, as well as all finger joint
angles of the hand. Trained purely in simulation, this framework skillfully closes the sim-to-real gap. To close the visual
sim-to-real gap, this framework uses a single depth image as input. To close the dynamics sim-to-real gap, this framework
circumvents continuous motor control with a direct mapping from pixel to Cartesian space inferred from the same depth
image. Finally, this framework demonstrates inter-robot generality by achieving over 92% real-world grasp success rates in
cluttered scenes with novel objects using two multi-fingered robotic hand-arm systems with different degrees of freedom.
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- Award ID(s):
- 1734557
- NSF-PAR ID:
- 10164432
- Date Published:
- Journal Name:
- Autonomous Robots
- ISSN:
- 0929-5593
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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There has been significant recent work on data-driven algorithms for learning general-purpose grasping policies. However, these policies can consis- tently fail to grasp challenging objects which are significantly out of the distribution of objects in the training data or which have very few high quality grasps. Moti- vated by such objects, we propose a novel problem setting, Exploratory Grasping, for efficiently discovering reliable grasps on an unknown polyhedral object via sequential grasping, releasing, and toppling. We formalize Exploratory Grasping as a Markov Decision Process where we assume that the robot can (1) distinguish stable poses of a polyhedral object of unknown geometry, (2) generate grasp can- didates on these poses and execute them, (3) determine whether each grasp is successful, and (4) release the object into a random new pose after a grasp success or topple the object after a grasp failure. We study the theoretical complexity of Exploratory Grasping in the context of reinforcement learning and present an efficient bandit-style algorithm, Bandits for Online Rapid Grasp Exploration Strategy (BORGES), which leverages the structure of the problem to efficiently discover high performing grasps for each object stable pose. BORGES can be used to complement any general-purpose grasping algorithm with any grasp modality (parallel-jaw, suction, multi-fingered, etc) to learn policies for objects in which they exhibit persistent failures. Simulation experiments suggest that BORGES can significantly outperform both general-purpose grasping pipelines and two other online learning algorithms and achieves performance within 5% of the optimal policy within 1000 and 8000 timesteps on average across 46 challenging objects from the Dex-Net adversarial and EGAD! object datasets, respectively. Initial physical experiments suggest that BORGES can improve grasp success rate by 45% over a Dex-Net baseline with just 200 grasp attempts in the real world. See https://tinyurl.com/exp-grasping for supplementary material and videos.more » « less
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1007/s10514-020-09907-y
