Zobrazeno 1 - 10
of 153
pro vyhledávání: '"Bera, Aniket"'
We propose a novel framework COLLAGE for generating collaborative agent-object-agent interactions by leveraging large language models (LLMs) and hierarchical motion-specific vector-quantized variational autoencoders (VQ-VAEs). Our model addresses the
Externí odkaz:
http://arxiv.org/abs/2409.20502
Autor:
Wu, Yi, Xiong, Zikang, Hu, Yiran, Iyengar, Shreyash S., Jiang, Nan, Bera, Aniket, Tan, Lin, Jagannathan, Suresh
Despite significant advancements in large language models (LLMs) that enhance robot agents' understanding and execution of natural language (NL) commands, ensuring the agents adhere to user-specified constraints remains challenging, particularly for
Externí odkaz:
http://arxiv.org/abs/2409.19471
Multi-Robot Informative Path Planning for Efficient Target Mapping using Deep Reinforcement Learning
Autonomous robots are being employed in several mapping and data collection tasks due to their efficiency and low labor costs. In these tasks, the robots are required to map targets-of-interest in an unknown environment while constrained to a given r
Externí odkaz:
http://arxiv.org/abs/2409.16967
Autor:
Punyamoorty, Vineet, Jutras-Dubé, Pascal, Zhang, Ruqi, Aggarwal, Vaneet, Conover, Damon, Bera, Aniket
By framing reinforcement learning as a sequence modeling problem, recent work has enabled the use of generative models, such as diffusion models, for planning. While these models are effective in predicting long-horizon state trajectories in determin
Externí odkaz:
http://arxiv.org/abs/2409.16950
We introduce Go-SLAM, a novel framework that utilizes 3D Gaussian Splatting SLAM to reconstruct dynamic environments while embedding object-level information within the scene representations. This framework employs advanced object segmentation techni
Externí odkaz:
http://arxiv.org/abs/2409.16944
A team of multiple robots seamlessly and safely working in human-filled public environments requires adaptive task allocation and socially-aware navigation that account for dynamic human behavior. Current approaches struggle with highly dynamic pedes
Externí odkaz:
http://arxiv.org/abs/2409.11561
The field of text-to-3D content generation has made significant progress in generating realistic 3D objects, with existing methodologies like Score Distillation Sampling (SDS) offering promising guidance. However, these methods often encounter the "J
Externí odkaz:
http://arxiv.org/abs/2409.06620
Publikováno v:
IROS 2024
While LLMs are proficient at processing text in human conversations, they often encounter difficulties with the nuances of verbal instructions and, thus, remain prone to hallucinate trust in human command. In this work, we present TrustNavGPT, an LLM
Externí odkaz:
http://arxiv.org/abs/2408.01867
Planning with generative models has emerged as an effective decision-making paradigm across a wide range of domains, including reinforcement learning and autonomous navigation. While continuous replanning at each timestep might seem intuitive because
Externí odkaz:
http://arxiv.org/abs/2408.01510
We propose accelerating the simulation of Lagrangian dynamics, such as fluid flows, granular flows, and elastoplasticity, with neural-operator-based reduced-order modeling. While full-order approaches simulate the physics of every particle within the
Externí odkaz:
http://arxiv.org/abs/2407.03925