Zobrazeno 1 - 10
of 1 347
pro vyhledávání: '"JAIN, DEEPALI"'
Autor:
Reid, Isaac, Dubey, Kumar Avinava, Jain, Deepali, Whitney, Will, Ahmed, Amr, Ainslie, Joshua, Bewley, Alex, Jacob, Mithun, Mehta, Aranyak, Rendleman, David, Schenck, Connor, Turner, Richard E., Wagner, René, Weller, Adrian, Choromanski, Krzysztof
When training transformers on graph-structured data, incorporating information about the underlying topology is crucial for good performance. Topological masking, a type of relative position encoding, achieves this by upweighting or downweighting att
Externí odkaz:
http://arxiv.org/abs/2410.03462
Autor:
D'Ambrosio, David B., Abeyruwan, Saminda, Graesser, Laura, Iscen, Atil, Amor, Heni Ben, Bewley, Alex, Reed, Barney J., Reymann, Krista, Takayama, Leila, Tassa, Yuval, Choromanski, Krzysztof, Coumans, Erwin, Jain, Deepali, Jaitly, Navdeep, Jaques, Natasha, Kataoka, Satoshi, Kuang, Yuheng, Lazic, Nevena, Mahjourian, Reza, Moore, Sherry, Oslund, Kenneth, Shankar, Anish, Sindhwani, Vikas, Vanhoucke, Vincent, Vesom, Grace, Xu, Peng, Sanketi, Pannag R.
Achieving human-level speed and performance on real world tasks is a north star for the robotics research community. This work takes a step towards that goal and presents the first learned robot agent that reaches amateur human-level performance in c
Externí odkaz:
http://arxiv.org/abs/2408.03906
Autor:
Whitney, William F., Varley, Jacob, Jain, Deepali, Choromanski, Krzysztof, Singh, Sumeet, Sindhwani, Vikas
We present High-Density Visual Particle Dynamics (HD-VPD), a learned world model that can emulate the physical dynamics of real scenes by processing massive latent point clouds containing 100K+ particles. To enable efficiency at this scale, we introd
Externí odkaz:
http://arxiv.org/abs/2406.19800
Autor:
Sehanobish, Arijit, Dubey, Avinava, Choromanski, Krzysztof, Chowdhury, Somnath Basu Roy, Jain, Deepali, Sindhwani, Vikas, Chaturvedi, Snigdha
Recent efforts to scale Transformer models have demonstrated rapid progress across a wide range of tasks (Wei et al., 2022). However, fine-tuning these models for downstream tasks is expensive due to their large parameter counts. Parameter-efficient
Externí odkaz:
http://arxiv.org/abs/2406.17740
Autor:
Varley, Jake, Singh, Sumeet, Jain, Deepali, Choromanski, Krzysztof, Zeng, Andy, Chowdhury, Somnath Basu Roy, Dubey, Avinava, Sindhwani, Vikas
We present an embodied AI system which receives open-ended natural language instructions from a human, and controls two arms to collaboratively accomplish potentially long-horizon tasks over a large workspace. Our system is modular: it deploys state
Externí odkaz:
http://arxiv.org/abs/2404.03570
Autor:
Leal, Isabel, Choromanski, Krzysztof, Jain, Deepali, Dubey, Avinava, Varley, Jake, Ryoo, Michael, Lu, Yao, Liu, Frederick, Sindhwani, Vikas, Vuong, Quan, Sarlos, Tamas, Oslund, Ken, Hausman, Karol, Rao, Kanishka
We present Self-Adaptive Robust Attention for Robotics Transformers (SARA-RT): a new paradigm for addressing the emerging challenge of scaling up Robotics Transformers (RT) for on-robot deployment. SARA-RT relies on the new method of fine-tuning prop
Externí odkaz:
http://arxiv.org/abs/2312.01990
Autor:
D'Ambrosio, David B., Abelian, Jonathan, Abeyruwan, Saminda, Ahn, Michael, Bewley, Alex, Boyd, Justin, Choromanski, Krzysztof, Cortes, Omar, Coumans, Erwin, Ding, Tianli, Gao, Wenbo, Graesser, Laura, Iscen, Atil, Jaitly, Navdeep, Jain, Deepali, Kangaspunta, Juhana, Kataoka, Satoshi, Kouretas, Gus, Kuang, Yuheng, Lazic, Nevena, Lynch, Corey, Mahjourian, Reza, Moore, Sherry Q., Nguyen, Thinh, Oslund, Ken, Reed, Barney J, Reymann, Krista, Sanketi, Pannag R., Shankar, Anish, Sermanet, Pierre, Sindhwani, Vikas, Singh, Avi, Vanhoucke, Vincent, Vesom, Grace, Xu, Peng
We present a deep-dive into a real-world robotic learning system that, in previous work, was shown to be capable of hundreds of table tennis rallies with a human and has the ability to precisely return the ball to desired targets. This system puts to
Externí odkaz:
http://arxiv.org/abs/2309.03315
Autor:
Abeyruwan, Saminda, Bewley, Alex, Boffi, Nicholas M., Choromanski, Krzysztof, D'Ambrosio, David, Jain, Deepali, Sanketi, Pannag, Shankar, Anish, Sindhwani, Vikas, Singh, Sumeet, Slotine, Jean-Jacques, Tu, Stephen
We address a benchmark task in agile robotics: catching objects thrown at high-speed. This is a challenging task that involves tracking, intercepting, and cradling a thrown object with access only to visual observations of the object and the proprioc
Externí odkaz:
http://arxiv.org/abs/2306.08205