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pro vyhledávání: '"Chang, Matthew P."'
Autor:
Khanna, Mukul, Ramrakhya, Ram, Chhablani, Gunjan, Yenamandra, Sriram, Gervet, Theophile, Chang, Matthew, Kira, Zsolt, Chaplot, Devendra Singh, Batra, Dhruv, Mottaghi, Roozbeh
The Embodied AI community has made significant strides in visual navigation tasks, exploring targets from 3D coordinates, objects, language descriptions, and images. However, these navigation models often handle only a single input modality as the ta
Externí odkaz:
http://arxiv.org/abs/2404.06609
A common failure mode for policies trained with imitation is compounding execution errors at test time. When the learned policy encounters states that are not present in the expert demonstrations, the policy fails, leading to degenerate behavior. The
Externí odkaz:
http://arxiv.org/abs/2402.17768
3D hand pose estimation in everyday egocentric images is challenging for several reasons: poor visual signal (occlusion from the object of interaction, low resolution & motion blur), large perspective distortion (hands are close to the camera), and l
Externí odkaz:
http://arxiv.org/abs/2312.06583
Autor:
Chang, Matthew, Gervet, Theophile, Khanna, Mukul, Yenamandra, Sriram, Shah, Dhruv, Min, So Yeon, Shah, Kavit, Paxton, Chris, Gupta, Saurabh, Batra, Dhruv, Mottaghi, Roozbeh, Malik, Jitendra, Chaplot, Devendra Singh
In deployment scenarios such as homes and warehouses, mobile robots are expected to autonomously navigate for extended periods, seamlessly executing tasks articulated in terms that are intuitively understandable by human operators. We present GO To A
Externí odkaz:
http://arxiv.org/abs/2311.06430
The analysis and use of egocentric videos for robotic tasks is made challenging by occlusion due to the hand and the visual mismatch between the human hand and a robot end-effector. In this sense, the human hand presents a nuisance. However, often ha
Externí odkaz:
http://arxiv.org/abs/2305.16301
Prior works for reconstructing hand-held objects from a single image train models on images paired with 3D shapes. Such data is challenging to gather in the real world at scale. Consequently, these approaches do not generalize well when presented wit
Externí odkaz:
http://arxiv.org/abs/2305.03036
Autor:
Chang, Matthew, Gupta, Saurabh
In this paper, we analyze the behavior of existing techniques and design new solutions for the problem of one-shot visual imitation. In this setting, an agent must solve a novel instance of a novel task given just a single visual demonstration. Our a
Externí odkaz:
http://arxiv.org/abs/2302.04856
This paper tackles the problem of learning value functions from undirected state-only experience (state transitions without action labels i.e. (s,s',r) tuples). We first theoretically characterize the applicability of Q-learning in this setting. We s
Externí odkaz:
http://arxiv.org/abs/2204.12458
Semantic cues and statistical regularities in real-world environment layouts can improve efficiency for navigation in novel environments. This paper learns and leverages such semantic cues for navigating to objects of interest in novel environments,
Externí odkaz:
http://arxiv.org/abs/2006.10034
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