1,029 research outputs found
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
Dynamic Mesh-Aware Radiance Fields
Embedding polygonal mesh assets within photorealistic Neural Radience Fields
(NeRF) volumes, such that they can be rendered and their dynamics simulated in
a physically consistent manner with the NeRF, is under-explored from the system
perspective of integrating NeRF into the traditional graphics pipeline. This
paper designs a two-way coupling between mesh and NeRF during rendering and
simulation. We first review the light transport equations for both mesh and
NeRF, then distill them into an efficient algorithm for updating radiance and
throughput along a cast ray with an arbitrary number of bounces. To resolve the
discrepancy between the linear color space that the path tracer assumes and the
sRGB color space that standard NeRF uses, we train NeRF with High Dynamic Range
(HDR) images. We also present a strategy to estimate light sources and cast
shadows on the NeRF. Finally, we consider how the hybrid surface-volumetric
formulation can be efficiently integrated with a high-performance physics
simulator that supports cloth, rigid and soft bodies. The full rendering and
simulation system can be run on a GPU at interactive rates. We show that a
hybrid system approach outperforms alternatives in visual realism for mesh
insertion, because it allows realistic light transport from volumetric NeRF
media onto surfaces, which affects the appearance of reflective/refractive
surfaces and illumination of diffuse surfaces informed by the dynamic scene.Comment: ICCV 202
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