49 research outputs found
Task-Driven Hybrid Model Reduction for Dexterous Manipulation
In contact-rich tasks, like dexterous manipulation, the hybrid nature of
making and breaking contact creates challenges for model representation and
control. For example, choosing and sequencing contact locations for in-hand
manipulation, where there are thousands of potential hybrid modes, is not
generally tractable. In this paper, we are inspired by the observation that far
fewer modes are actually necessary to accomplish many tasks. Building on our
prior work learning hybrid models, represented as linear complementarity
systems, we find a reduced-order hybrid model requiring only a limited number
of task-relevant modes. This simplified representation, in combination with
model predictive control, enables real-time control yet is sufficient for
achieving high performance. We demonstrate the proposed method first on
synthetic hybrid systems, reducing the mode count by multiple orders of
magnitude while achieving task performance loss of less than 5%. We also apply
the proposed method to a three-fingered robotic hand manipulating a previously
unknown object. With no prior knowledge, we achieve state-of-the-art
closed-loop performance within a few minutes of online learning, by collecting
only a few thousand environment samples.Comment: Reproducing code:
https://github.com/wanxinjin/Task-Driven-Hybrid-Reduction. This is a
preprint. The published version can be accessed at IEEE Transactions on
Robotic
Optimal Reduced-order Modeling of Bipedal Locomotion
State-of-the-art approaches to legged locomotion are widely dependent on the
use of models like the linear inverted pendulum (LIP) and the spring-loaded
inverted pendulum (SLIP), popular because their simplicity enables a wide array
of tools for planning, control, and analysis. However, they inevitably limit
the ability to execute complex tasks or agile maneuvers. In this work, we aim
to automatically synthesize models that remain low-dimensional but retain the
capabilities of the high-dimensional system. For example, if one were to
restore a small degree of complexity to LIP, SLIP, or a similar model, our
approach discovers the form of that additional complexity which optimizes
performance. In this paper, we define a class of reduced-order models and
provide an algorithm for optimization within this class. To demonstrate our
method, we optimize models for walking at a range of speeds and ground
inclines, for both a five-link model and the Cassie bipedal robot.Comment: Submitted to ICRA 202
Simultaneous Learning of Contact and Continuous Dynamics
Robotic manipulation can greatly benefit from the data efficiency,
robustness, and predictability of model-based methods if robots can quickly
generate models of novel objects they encounter. This is especially difficult
when effects like complex joint friction lack clear first-principles models and
are usually ignored by physics simulators. Further, numerically-stiff contact
dynamics can make common model-building approaches struggle. We propose a
method to simultaneously learn contact and continuous dynamics of a novel,
possibly multi-link object by observing its motion through contact-rich
trajectories. We formulate a system identification process with a loss that
infers unmeasured contact forces, penalizing their violation of physical
constraints and laws of motion given current model parameters. Our loss is
unlike prediction-based losses used in differentiable simulation. Using a new
dataset of real articulated object trajectories and an existing cube toss
dataset, our method outperforms differentiable simulation and end-to-end
alternatives with more data efficiency. See our project page for code,
datasets, and media: https://sites.google.com/view/continuous-contact-nets/homeComment: 13 pages, 5 figures. Accepted to Conference on Robot Learning (CoRL)
2023. Project webpage with code, datasets, media, and OpenReview link at
https://sites.google.com/view/continuous-contact-nets/hom
A direct method for trajectory optimization of rigid bodies through contact
Direct methods for trajectory optimization are widely used for planning locally optimal trajectories of robotic systems. Many critical tasks, such as locomotion and manipulation, often involve impacting the ground or objects in the environment. Most state-of-the-art techniques treat the discontinuous dynamics that result from impacts as discrete modes and restrict the search for a complete path to a specified sequence through these modes. Here we present a novel method for trajectory planning of rigid-body systems that contact their environment through inelastic impacts and Coulomb friction. This method eliminates the requirement for a priori mode ordering. Motivated by the formulation of multi-contact dynamics as a Linear Complementarity Problem for forward simulation, the proposed algorithm poses the optimization problem as a Mathematical Program with Complementarity Constraints. We leverage Sequential Quadratic Programming to naturally resolve contact constraint forces while simultaneously optimizing a trajectory that satisfies the complementarity constraints. The method scales well to high-dimensional systems with large numbers of possible modes. We demonstrate the approach on four increasingly complex systems: rotating a pinned object with a finger, simple grasping and manipulation, planar walking with the Spring Flamingo robot, and high-speed bipedal running on the FastRunner platform.United States. Defense Advanced Research Projects Agency. Maximum Mobility and Manipulation Program (Grant W91CRB-11-1-0001)National Science Foundation (U.S.) (Grant IIS-0746194)National Science Foundation (U.S.) (Grant IIS-1161909)National Science Foundation (U.S.) (Grant IIS-0915148
Adaptive Contact-Implicit Model Predictive Control with Online Residual Learning
The hybrid nature of multi-contact robotic systems, due to making and
breaking contact with the environment, creates significant challenges for
high-quality control. Existing model-based methods typically rely on either
good prior knowledge of the multi-contact model or require significant offline
model tuning effort, thus resulting in low adaptability and robustness. In this
paper, we propose a real-time adaptive multi-contact model predictive control
framework, which enables online adaption of the hybrid multi-contact model and
continuous improvement of the control performance for contact-rich tasks. This
framework includes an adaption module, which continuously learns a residual of
the hybrid model to minimize the gap between the prior model and reality, and a
real-time multi-contact MPC controller. We demonstrated the effectiveness of
the framework in synthetic examples, and applied it on hardware to solve
contact-rich manipulation tasks, where a robot uses its end-effector to roll
different unknown objects on a table to track given paths. The hardware
experiments show that with a rough prior model, the multi-contact MPC
controller adapts itself on-the-fly with an adaption rate around 20 Hz and
successfully manipulates previously unknown objects with non-smooth surface
geometries.Comment: Wei-Cheng Huang and Alp Aydinoglu contributed equally to this work.
ICRA 2024 Final Submissio