Search Results for author: Alexander Clegg

Found 8 papers, 4 papers with code

Controllable Human-Object Interaction Synthesis

no code implementations6 Dec 2023 Jiaman Li, Alexander Clegg, Roozbeh Mottaghi, Jiajun Wu, Xavier Puig, C. Karen Liu

Naively applying a diffusion model fails to predict object motion aligned with the input waypoints and cannot ensure the realism of interactions that require precise hand-object contact and appropriate contact grounded by the floor.

Human-Object Interaction Detection Object

Habitat Synthetic Scenes Dataset (HSSD-200): An Analysis of 3D Scene Scale and Realism Tradeoffs for ObjectGoal Navigation

no code implementations20 Jun 2023 Mukul Khanna, Yongsen Mao, Hanxiao Jiang, Sanjay Haresh, Brennan Shacklett, Dhruv Batra, Alexander Clegg, Eric Undersander, Angel X. Chang, Manolis Savva

Surprisingly, we observe that agents trained on just 122 scenes from our dataset outperform agents trained on 10, 000 scenes from the ProcTHOR-10K dataset in terms of zero-shot generalization in real-world scanned environments.

Navigate Zero-shot Generalization

CIRCLE: Capture In Rich Contextual Environments

1 code implementation CVPR 2023 Joao Pedro Araujo, Jiaman Li, Karthik Vetrivel, Rishi Agarwal, Deepak Gopinath, Jiajun Wu, Alexander Clegg, C. Karen Liu

Leveraging our dataset, the model learns to use ego-centric scene information to achieve nontrivial reaching tasks in the context of complex 3D scenes.

Learning to Transfer In-Hand Manipulations Using a Greedy Shape Curriculum

no code implementations14 Mar 2023 Yunbo Zhang, Alexander Clegg, Sehoon Ha, Greg Turk, Yuting Ye

In-hand object manipulation is challenging to simulate due to complex contact dynamics, non-repetitive finger gaits, and the need to indirectly control unactuated objects.

Imitation Learning Object

Learning to Collaborate from Simulation for Robot-Assisted Dressing

no code implementations14 Sep 2019 Alexander Clegg, Zackory Erickson, Patrick Grady, Greg Turk, Charles C. Kemp, C. Karen Liu

We investigated the application of haptic feedback control and deep reinforcement learning (DRL) to robot-assisted dressing.

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