Zobrazeno 1 - 10
of 235
pro vyhledávání: '"Biswas, Joydeep"'
Autor:
Hsu, Cheng-Chun, Abbatematteo, Ben, Jiang, Zhenyu, Zhu, Yuke, Martín-Martín, Roberto, Biswas, Joydeep
Sequentially interacting with articulated objects is crucial for a mobile manipulator to operate effectively in everyday environments. To enable long-horizon tasks involving articulated objects, this study explores building scene-level articulation m
Externí odkaz:
http://arxiv.org/abs/2409.16473
Navigating and understanding complex environments over extended periods of time is a significant challenge for robots. People interacting with the robot may want to ask questions like where something happened, when it occurred, or how long ago it too
Externí odkaz:
http://arxiv.org/abs/2409.13682
CLOVER: Context-aware Long-term Object Viewpoint- and Environment- Invariant Representation Learning
In many applications, robots can benefit from object-level understanding of their environments, including the ability to distinguish object instances and re-identify previously seen instances. Object re-identification is challenging across different
Externí odkaz:
http://arxiv.org/abs/2407.09718
Autonomous mobile robots deployed in urban environments must be context-aware, i.e., able to distinguish between different semantic entities, and robust to occlusions. Current approaches like semantic scene completion (SSC) require pre-enumerating th
Externí odkaz:
http://arxiv.org/abs/2407.03425
Large language models (LLMs) have shown great promise at generating robot programs from natural language given domain-specific robot application programming interfaces (APIs). However, the performance gap between proprietary LLMs and smaller open-wei
Externí odkaz:
http://arxiv.org/abs/2405.20179
Imitation Learning (IL) strategies are used to generate policies for robot motion planning and navigation by learning from human trajectories. Recently, there has been a lot of excitement in applying IL in social interactions arising in urban environ
Externí odkaz:
http://arxiv.org/abs/2405.16439
Among approaches for provably safe reinforcement learning, Model Predictive Shielding (MPS) has proven effective at complex tasks in continuous, high-dimensional state spaces, by leveraging a backup policy to ensure safety when the learned policy att
Externí odkaz:
http://arxiv.org/abs/2405.13863
This paper addresses the problem of preference learning, which aims to learn user-specific preferences (e.g., "good parking spot", "convenient drop-off location") from visual input. Despite its similarity to learning factual concepts (e.g., "red cube
Externí odkaz:
http://arxiv.org/abs/2403.16689
HRI research using autonomous robots in real-world settings can produce results with the highest ecological validity of any study modality, but many difficulties limit such studies' feasibility and effectiveness. We propose Vid2Real HRI, a research f
Externí odkaz:
http://arxiv.org/abs/2403.15798
Autor:
Hwang, Hochul, Jung, Hee-Tae, Giudice, Nicholas A, Biswas, Joydeep, Lee, Sunghoon Ivan, Kim, Donghyun
Dog guides are favored by blind and low-vision (BLV) individuals for their ability to enhance independence and confidence by reducing safety concerns and increasing navigation efficiency compared to traditional mobility aids. However, only a relative
Externí odkaz:
http://arxiv.org/abs/2402.06790