62 research outputs found
XSkill: Cross Embodiment Skill Discovery
Human demonstration videos are a widely available data source for robot
learning and an intuitive user interface for expressing desired behavior.
However, directly extracting reusable robot manipulation skills from
unstructured human videos is challenging due to the big embodiment difference
and unobserved action parameters. To bridge this embodiment gap, this paper
introduces XSkill, an imitation learning framework that 1) discovers a
cross-embodiment representation called skill prototypes purely from unlabeled
human and robot manipulation videos, 2) transfers the skill representation to
robot actions using conditional diffusion policy, and finally, 3) composes the
learned skill to accomplish unseen tasks specified by a human prompt video. Our
experiments in simulation and real-world environments show that the discovered
skill prototypes facilitate both skill transfer and composition for unseen
tasks, resulting in a more general and scalable imitation learning framework.
The benchmark, code, and qualitative results are on
https://xskill.cs.columbia.edu
Unsupervised Discovery of Parts, Structure, and Dynamics
Humans easily recognize object parts and their hierarchical structure by
watching how they move; they can then predict how each part moves in the
future. In this paper, we propose a novel formulation that simultaneously
learns a hierarchical, disentangled object representation and a dynamics model
for object parts from unlabeled videos. Our Parts, Structure, and Dynamics
(PSD) model learns to, first, recognize the object parts via a layered image
representation; second, predict hierarchy via a structural descriptor that
composes low-level concepts into a hierarchical structure; and third, model the
system dynamics by predicting the future. Experiments on multiple real and
synthetic datasets demonstrate that our PSD model works well on all three
tasks: segmenting object parts, building their hierarchical structure, and
capturing their motion distributions.Comment: ICLR 2019. The first two authors contributed equally to this wor
A Mixed-Integer SDP Solution Approach to Distributionally Robust Unit Commitment with Second Order Moment Constraints
A power system unit commitment (UC) problem considering uncertainties of
renewable energy sources is investigated in this paper, through a
distributionally robust optimization approach. We assume that the first and
second order moments of stochastic parameters can be inferred from historical
data, and then employed to model the set of probability distributions. The
resulting problem is a two-stage distributionally robust unit commitment with
second order moment constraints, and we show that it can be recast as a
mixed-integer semidefinite programming (MI-SDP) with finite constraints. The
solution algorithm of the problem comprises solving a series of relaxed MI-SDPs
and a subroutine of feasibility checking and vertex generation. Based on the
verification of strong duality of the semidefinite programming (SDP) problems,
we propose a cutting plane algorithm for solving the MI-SDPs; we also introduce
a SDP relaxation for the feasibility checking problem, which is an intractable
biconvex optimization. Experimental results on a IEEE 6-bus system are
presented, showing that without any tunings of parameters, the real-time
operation cost of distributionally robust UC method outperforms those of
deterministic UC and two-stage robust UC methods in general, and our method
also enjoys higher reliability of dispatch operation
Diffusion Policy: Visuomotor Policy Learning via Action Diffusion
This paper introduces Diffusion Policy, a new way of generating robot
behavior by representing a robot's visuomotor policy as a conditional denoising
diffusion process. We benchmark Diffusion Policy across 11 different tasks from
4 different robot manipulation benchmarks and find that it consistently
outperforms existing state-of-the-art robot learning methods with an average
improvement of 46.9%. Diffusion Policy learns the gradient of the
action-distribution score function and iteratively optimizes with respect to
this gradient field during inference via a series of stochastic Langevin
dynamics steps. We find that the diffusion formulation yields powerful
advantages when used for robot policies, including gracefully handling
multimodal action distributions, being suitable for high-dimensional action
spaces, and exhibiting impressive training stability. To fully unlock the
potential of diffusion models for visuomotor policy learning on physical
robots, this paper presents a set of key technical contributions including the
incorporation of receding horizon control, visual conditioning, and the
time-series diffusion transformer. We hope this work will help motivate a new
generation of policy learning techniques that are able to leverage the powerful
generative modeling capabilities of diffusion models. Code, data, and training
details will be publicly available
Towards Generalist Robots: A Promising Paradigm via Generative Simulation
This document serves as a position paper that outlines the authors' vision
for a potential pathway towards generalist robots. The purpose of this document
is to share the excitement of the authors with the community and highlight a
promising research direction in robotics and AI. The authors believe the
proposed paradigm is a feasible path towards accomplishing the long-standing
goal of robotics research: deploying robots, or embodied AI agents more
broadly, in various non-factory real-world settings to perform diverse tasks.
This document presents a specific idea for mining knowledge in the latest
large-scale foundation models for robotics research. Instead of directly using
or adapting these models to produce low-level policies and actions, it
advocates for a fully automated generative pipeline (termed as generative
simulation), which uses these models to generate diversified tasks, scenes and
training supervisions at scale, thereby scaling up low-level skill learning and
ultimately leading to a foundation model for robotics that empowers generalist
robots. The authors are actively pursuing this direction, but in the meantime,
they recognize that the ambitious goal of building generalist robots with
large-scale policy training demands significant resources such as computing
power and hardware, and research groups in academia alone may face severe
resource constraints in implementing the entire vision. Therefore, the authors
believe sharing their thoughts at this early stage could foster discussions,
attract interest towards the proposed pathway and related topics from industry
groups, and potentially spur significant technical advancements in the field
Robots That Ask For Help: Uncertainty Alignment for Large Language Model Planners
Large language models (LLMs) exhibit a wide range of promising capabilities
-- from step-by-step planning to commonsense reasoning -- that may provide
utility for robots, but remain prone to confidently hallucinated predictions.
In this work, we present KnowNo, which is a framework for measuring and
aligning the uncertainty of LLM-based planners such that they know when they
don't know and ask for help when needed. KnowNo builds on the theory of
conformal prediction to provide statistical guarantees on task completion while
minimizing human help in complex multi-step planning settings. Experiments
across a variety of simulated and real robot setups that involve tasks with
different modes of ambiguity (e.g., from spatial to numeric uncertainties, from
human preferences to Winograd schemas) show that KnowNo performs favorably over
modern baselines (which may involve ensembles or extensive prompt tuning) in
terms of improving efficiency and autonomy, while providing formal assurances.
KnowNo can be used with LLMs out of the box without model-finetuning, and
suggests a promising lightweight approach to modeling uncertainty that can
complement and scale with the growing capabilities of foundation models.
Website: https://robot-help.github.ioComment: Conference on Robot Learning (CoRL) 2023, Oral Presentatio
Synthesis, Biological Evaluation and Mechanism Studies of Deoxytylophorinine and Its Derivatives as Potential Anticancer Agents
Previous studies indicated that (+)-13a-(S)-Deoxytylophorinine (1) showed profound anti-cancer activities both in vitro and in vivo and could penetrate the blood brain barrier to distribute well in brain tissues. CNS toxicity, one of the main factors to hinder the development of phenanthroindolizidines, was not obviously found in 1. Based on its fascinating activities, thirty-four derivatives were designed, synthesized; their cytotoxic activities in vitro were tested to discover more excellent anticancer agents. Considering the distinctive mechanism of 1 and interesting SAR of deoxytylophorinine and its derivatives, the specific impacts of these compounds on cellular progress as cell signaling transduction pathways and cell cycle were proceeded with seven representative compounds. 1 as well as three most potent compounds, 9, 32, 33, and three less active compounds, 12, 16, 35, were selected to proform this study to have a relatively deep view of cancer cell growth-inhibitory characteristics. It was found that the expressions of phospho-Akt, Akt, phospho-ERK, and ERK in A549 cells were greater down-regulated by the potent compounds than by the less active compounds in the Western blot analysis. To the best of our knowledge, this is the first report describing phenanthroindolizidines alkaloids display influence on the crucial cell signaling proteins, ERK. Moreover, the expressions of cyclin A, cyclin D1 and CDK2 proteins depressed more dramatically when the cells were treated with 1, 9, 32, and 33. Then, these four excellent compounds were subjected to flow cytometric analysis, and an increase in S-phase was observed in A549 cells. Since the molecular level assay results of Western blot for phospho-Akt, Akt, phospho-ERK, ERK, and cyclins were relevant to the potency of compounds in cellular level, we speculated that this series of compounds exhibit anticancer activities through blocking PI3K and MAPK signaling transduction pathways and interfering with the cell cycle progression
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