27 research outputs found
MovingParts: Motion-based 3D Part Discovery in Dynamic Radiance Field
We present MovingParts, a NeRF-based method for dynamic scene reconstruction
and part discovery. We consider motion as an important cue for identifying
parts, that all particles on the same part share the common motion pattern.
From the perspective of fluid simulation, existing deformation-based methods
for dynamic NeRF can be seen as parameterizing the scene motion under the
Eulerian view, i.e., focusing on specific locations in space through which the
fluid flows as time passes. However, it is intractable to extract the motion of
constituting objects or parts using the Eulerian view representation. In this
work, we introduce the dual Lagrangian view and enforce representations under
the Eulerian/Lagrangian views to be cycle-consistent. Under the Lagrangian
view, we parameterize the scene motion by tracking the trajectory of particles
on objects. The Lagrangian view makes it convenient to discover parts by
factorizing the scene motion as a composition of part-level rigid motions.
Experimentally, our method can achieve fast and high-quality dynamic scene
reconstruction from even a single moving camera, and the induced part-based
representation allows direct applications of part tracking, animation, 3D scene
editing, etc.Comment: 10 page
Reparameterized Policy Learning for Multimodal Trajectory Optimization
We investigate the challenge of parametrizing policies for reinforcement
learning (RL) in high-dimensional continuous action spaces. Our objective is to
develop a multimodal policy that overcomes limitations inherent in the
commonly-used Gaussian parameterization. To achieve this, we propose a
principled framework that models the continuous RL policy as a generative model
of optimal trajectories. By conditioning the policy on a latent variable, we
derive a novel variational bound as the optimization objective, which promotes
exploration of the environment. We then present a practical model-based RL
method, called Reparameterized Policy Gradient (RPG), which leverages the
multimodal policy parameterization and learned world model to achieve strong
exploration capabilities and high data efficiency. Empirical results
demonstrate that our method can help agents evade local optima in tasks with
dense rewards and solve challenging sparse-reward environments by incorporating
an object-centric intrinsic reward. Our method consistently outperforms
previous approaches across a range of tasks. Code and supplementary materials
are available on the project page https://haosulab.github.io/RPG
Deductive Verification of Chain-of-Thought Reasoning
Large Language Models (LLMs) significantly benefit from Chain-of-Thought
(CoT) prompting in performing various reasoning tasks. While CoT allows models
to produce more comprehensive reasoning processes, its emphasis on intermediate
reasoning steps can inadvertently introduce hallucinations and accumulated
errors, thereby limiting models' ability to solve complex reasoning tasks.
Inspired by how humans engage in careful and meticulous deductive logical
reasoning processes to solve tasks, we seek to enable language models to
perform explicit and rigorous deductive reasoning, and also ensure the
trustworthiness of their reasoning process through self-verification. However,
directly verifying the validity of an entire deductive reasoning process is
challenging, even with advanced models like ChatGPT. In light of this, we
propose to decompose a reasoning verification process into a series of
step-by-step subprocesses, each only receiving their necessary context and
premises. To facilitate this procedure, we propose Natural Program, a natural
language-based deductive reasoning format. Our approach enables models to
generate precise reasoning steps where subsequent steps are more rigorously
grounded on prior steps. It also empowers language models to carry out
reasoning self-verification in a step-by-step manner. By integrating this
verification process into each deductive reasoning stage, we significantly
enhance the rigor and trustfulness of generated reasoning steps. Along this
process, we also improve the answer correctness on complex reasoning tasks.
Code will be released at https://github.com/lz1oceani/verify_cot
Utilization of Multilevel Flow Modelling to Support Passive Safety System Reliability Assessment
RoboCraft: Learning to See, Simulate, and Shape Elasto-Plastic Objects with Graph Networks
Modeling and manipulating elasto-plastic objects are essential capabilities
for robots to perform complex industrial and household interaction tasks (e.g.,
stuffing dumplings, rolling sushi, and making pottery). However, due to the
high degree of freedom of elasto-plastic objects, significant challenges exist
in virtually every aspect of the robotic manipulation pipeline, e.g.,
representing the states, modeling the dynamics, and synthesizing the control
signals. We propose to tackle these challenges by employing a particle-based
representation for elasto-plastic objects in a model-based planning framework.
Our system, RoboCraft, only assumes access to raw RGBD visual observations. It
transforms the sensing data into particles and learns a particle-based dynamics
model using graph neural networks (GNNs) to capture the structure of the
underlying system. The learned model can then be coupled with model-predictive
control (MPC) algorithms to plan the robot's behavior. We show through
experiments that with just 10 minutes of real-world robotic interaction data,
our robot can learn a dynamics model that can be used to synthesize control
signals to deform elasto-plastic objects into various target shapes, including
shapes that the robot has never encountered before. We perform systematic
evaluations in both simulation and the real world to demonstrate the robot's
manipulation capabilities and ability to generalize to a more complex action
space, different tool shapes, and a mixture of motion modes. We also conduct
comparisons between RoboCraft and untrained human subjects controlling the
gripper to manipulate deformable objects in both simulation and the real world.
Our learned model-based planning framework is comparable to and sometimes
better than human subjects on the tested tasks.Comment: Accepted by Robotics: Science and Systems 2022; Project page:
http://hxu.rocks/robocraft
Planning with Spatial-Temporal Abstraction from Point Clouds for Deformable Object Manipulation
Effective planning of long-horizon deformable object manipulation requires
suitable abstractions at both the spatial and temporal levels. Previous methods
typically either focus on short-horizon tasks or make strong assumptions that
full-state information is available, which prevents their use on deformable
objects. In this paper, we propose PlAnning with Spatial-Temporal Abstraction
(PASTA), which incorporates both spatial abstraction (reasoning about objects
and their relations to each other) and temporal abstraction (reasoning over
skills instead of low-level actions). Our framework maps high-dimension 3D
observations such as point clouds into a set of latent vectors and plans over
skill sequences on top of the latent set representation. We show that our
method can effectively perform challenging sequential deformable object
manipulation tasks in the real world, which require combining multiple tool-use
skills such as cutting with a knife, pushing with a pusher, and spreading the
dough with a roller.Comment: Published at the Conference on Robot Learning (CoRL 2022
Impact of Fluorination Degree of Ether-Based Electrolyte Solvent on Li-metal Battery Performance
Electrolytes using fluorinated solvents have proven effective in improving the cycling life of Li-metal batteries, by forming a robust solid-electrolyte interphase through decomposition of anion and fluorinated solvent molecules. Herein, we modulated the fluorination degree of ether-based electrolyte solvents to investigate their performance in Li-metal batteries. We tuned the fluorination degree by installing a monofluorine substituent on one ethoxy group of 1,2-diethoxyethane (DEE) and varying the fluorination degree on the other one, providing three fluorinated DEE solvent molecules (i.e., F1F0, F1F1 and F1F2) with a relatively low fluorination degree. All the three electrolytes showed improved solvation strength and ionic conductivities compared with previous highly fluorinated DEE elec-trolytes, while retaining good oxidative stability. Full cell test using Li-metal anode and nickel-rich cathode revealed that a higher degree of fluorination is beneficial to the cycling performance, and the cycling stability follows F1F0 < F1F1 < F1F2. Specifically, F1F0 exhibited poor cycling stability due to its instability against both anode and cathode. While F1F1 and F1F2 both showed good stability against Li-metal anode, their relative long-term oxidative stability was responsive for the distinct performance, in which the cycle numbers at 80% capacity retention for F1F1 and F1F2 were ~20 and ~80, respectively. This work shows the importance to modulate the fluorination degree of elec-trolyte solvents, and this approach is suitable for various cathode materials