21 research outputs found
DiffMimic: Efficient Motion Mimicking with Differentiable Physics
Motion mimicking is a foundational task in physics-based character animation.
However, most existing motion mimicking methods are built upon reinforcement
learning (RL) and suffer from heavy reward engineering, high variance, and slow
convergence with hard explorations. Specifically, they usually take tens of
hours or even days of training to mimic a simple motion sequence, resulting in
poor scalability. In this work, we leverage differentiable physics simulators
(DPS) and propose an efficient motion mimicking method dubbed DiffMimic. Our
key insight is that DPS casts a complex policy learning task to a much simpler
state matching problem. In particular, DPS learns a stable policy by analytical
gradients with ground-truth physical priors hence leading to significantly
faster and stabler convergence than RL-based methods. Moreover, to escape from
local optima, we utilize a Demonstration Replay mechanism to enable stable
gradient backpropagation in a long horizon. Extensive experiments on standard
benchmarks show that DiffMimic has a better sample efficiency and time
efficiency than existing methods (e.g., DeepMimic). Notably, DiffMimic allows a
physically simulated character to learn Backflip after 10 minutes of training
and be able to cycle it after 3 hours of training, while the existing approach
may require about a day of training to cycle Backflip. More importantly, we
hope DiffMimic can benefit more differentiable animation systems with
techniques like differentiable clothes simulation in future research.Comment: ICLR 2023 Code is at https://github.com/jiawei-ren/diffmimic Project
page is at https://diffmimic.github.io
What Truly Matters in Trajectory Prediction for Autonomous Driving?
In the autonomous driving system, trajectory prediction plays a vital role in
ensuring safety and facilitating smooth navigation. However, we observe a
substantial discrepancy between the accuracy of predictors on fixed datasets
and their driving performance when used in downstream tasks. This discrepancy
arises from two overlooked factors in the current evaluation protocols of
trajectory prediction: 1) the dynamics gap between the dataset and real driving
scenario; and 2) the computational efficiency of predictors. In real-world
scenarios, prediction algorithms influence the behavior of autonomous vehicles,
which, in turn, alter the behaviors of other agents on the road. This
interaction results in predictor-specific dynamics that directly impact
prediction results. As other agents' responses are predetermined on datasets, a
significant dynamics gap arises between evaluations conducted on fixed datasets
and actual driving scenarios. Furthermore, focusing solely on accuracy fails to
address the demand for computational efficiency, which is critical for the
real-time response required by the autonomous driving system. Therefore, in
this paper, we demonstrate that an interactive, task-driven evaluation approach
for trajectory prediction is crucial to reflect its efficacy for autonomous
driving
DaXBench: Benchmarking Deformable Object Manipulation with Differentiable Physics
Deformable Object Manipulation (DOM) is of significant importance to both
daily and industrial applications. Recent successes in differentiable physics
simulators allow learning algorithms to train a policy with analytic gradients
through environment dynamics, which significantly facilitates the development
of DOM algorithms. However, existing DOM benchmarks are either
single-object-based or non-differentiable. This leaves the questions of 1) how
a task-specific algorithm performs on other tasks and 2) how a
differentiable-physics-based algorithm compares with the non-differentiable
ones in general. In this work, we present DaXBench, a differentiable DOM
benchmark with a wide object and task coverage. DaXBench includes 9 challenging
high-fidelity simulated tasks, covering rope, cloth, and liquid manipulation
with various difficulty levels. To better understand the performance of general
algorithms on different DOM tasks, we conduct comprehensive experiments over
representative DOM methods, ranging from planning to imitation learning and
reinforcement learning. In addition, we provide careful empirical studies of
existing decision-making algorithms based on differentiable physics, and
discuss their limitations, as well as potential future directions.Comment: ICLR 2023 (Oral