1,179 research outputs found
Dexterous Soft Hands Linearize Feedback-Control for In-Hand Manipulation
This paper presents a feedback-control framework for in-hand manipulation
(IHM) with dexterous soft hands that enables the acquisition of manipulation
skills in the real-world within minutes. We choose the deformation state of the
soft hand as the control variable. To control for a desired deformation state,
we use coarsely approximated Jacobians of the actuation-deformation dynamics.
These Jacobian are obtained via explorative actions. This is enabled by the
self-stabilizing properties of compliant hands, which allow us to use linear
feedback control in the presence of complex contact dynamics. To evaluate the
effectiveness of our approach, we show the generalization capabilities for a
learned manipulation skill to variations in object size by 100 %, 360 degree
changes in palm inclination and to disabling up to 50 % of the involved
actuators. In addition, complex manipulations can be obtained by sequencing
such feedback-skills.Comment: Accepted at 2023 IEEE/RSJ International Conference on Intelligent
Robots and Systems (IROS
Sequential Dexterity: Chaining Dexterous Policies for Long-Horizon Manipulation
Many real-world manipulation tasks consist of a series of subtasks that are
significantly different from one another. Such long-horizon, complex tasks
highlight the potential of dexterous hands, which possess adaptability and
versatility, capable of seamlessly transitioning between different modes of
functionality without the need for re-grasping or external tools. However, the
challenges arise due to the high-dimensional action space of dexterous hand and
complex compositional dynamics of the long-horizon tasks. We present Sequential
Dexterity, a general system based on reinforcement learning (RL) that chains
multiple dexterous policies for achieving long-horizon task goals. The core of
the system is a transition feasibility function that progressively finetunes
the sub-policies for enhancing chaining success rate, while also enables
autonomous policy-switching for recovery from failures and bypassing redundant
stages. Despite being trained only in simulation with a few task objects, our
system demonstrates generalization capability to novel object shapes and is
able to zero-shot transfer to a real-world robot equipped with a dexterous
hand. More details and video results could be found at
https://sequential-dexterity.github.ioComment: CoRL 202
Getting the Ball Rolling: Learning a Dexterous Policy for a Biomimetic Tendon-Driven Hand with Rolling Contact Joints
Biomimetic, dexterous robotic hands have the potential to replicate much of
the tasks that a human can do, and to achieve status as a general manipulation
platform. Recent advances in reinforcement learning (RL) frameworks have
achieved remarkable performance in quadrupedal locomotion and dexterous
manipulation tasks. Combined with GPU-based highly parallelized simulations
capable of simulating thousands of robots in parallel, RL-based controllers
have become more scalable and approachable. However, in order to bring
RL-trained policies to the real world, we require training frameworks that
output policies that can work with physical actuators and sensors as well as a
hardware platform that can be manufactured with accessible materials yet is
robust enough to run interactive policies. This work introduces the biomimetic
tendon-driven Faive Hand and its system architecture, which uses tendon-driven
rolling contact joints to achieve a 3D printable, robust high-DoF hand design.
We model each element of the hand and integrate it into a GPU simulation
environment to train a policy with RL, and achieve zero-shot transfer of a
dexterous in-hand sphere rotation skill to the physical robot hand.Comment: for project website, see https://srl-ethz.github.io/get-ball-rolling/
. for video, see https://youtu.be/YahsMhqNU8o . Submitted to the 2023
IEEE-RAS International Conference on Humanoid Robot
MyoDex: A Generalizable Prior for Dexterous Manipulation
Human dexterity is a hallmark of motor control. Our hands can rapidly
synthesize new behaviors despite the complexity (multi-articular and
multi-joints, with 23 joints controlled by more than 40 muscles) of
musculoskeletal sensory-motor circuits. In this work, we take inspiration from
how human dexterity builds on a diversity of prior experiences, instead of
being acquired through a single task. Motivated by this observation, we set out
to develop agents that can build upon their previous experience to quickly
acquire new (previously unattainable) behaviors. Specifically, our approach
leverages multi-task learning to implicitly capture task-agnostic behavioral
priors (MyoDex) for human-like dexterity, using a physiologically realistic
human hand model - MyoHand. We demonstrate MyoDex's effectiveness in few-shot
generalization as well as positive transfer to a large repertoire of unseen
dexterous manipulation tasks. Agents leveraging MyoDex can solve approximately
3x more tasks, and 4x faster in comparison to a distillation baseline. While
prior work has synthesized single musculoskeletal control behaviors, MyoDex is
the first generalizable manipulation prior that catalyzes the learning of
dexterous physiological control across a large variety of contact-rich
behaviors. We also demonstrate the effectiveness of our paradigms beyond
musculoskeletal control towards the acquisition of dexterity in 24 DoF Adroit
Hand. Website: https://sites.google.com/view/myodexComment: Accepted to the 40th International Conference on Machine Learning
(2023
Dynamic Handover: Throw and Catch with Bimanual Hands
Humans throw and catch objects all the time. However, such a seemingly common
skill introduces a lot of challenges for robots to achieve: The robots need to
operate such dynamic actions at high-speed, collaborate precisely, and interact
with diverse objects. In this paper, we design a system with two multi-finger
hands attached to robot arms to solve this problem. We train our system using
Multi-Agent Reinforcement Learning in simulation and perform Sim2Real transfer
to deploy on the real robots. To overcome the Sim2Real gap, we provide multiple
novel algorithm designs including learning a trajectory prediction model for
the object. Such a model can help the robot catcher has a real-time estimation
of where the object will be heading, and then react accordingly. We conduct our
experiments with multiple objects in the real-world system, and show
significant improvements over multiple baselines. Our project page is available
at \url{https://binghao-huang.github.io/dynamic_handover/}.Comment: Accepted at CoRL 2023.
https://binghao-huang.github.io/dynamic_handover
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