22 citations · 73 across the 23 of their papers we have counts for
32 papers
Learning Realistic Athletic Sprinting Without Demonstrations
William Wang, Nicholas Bianco, Guy Tevet +4
We present a muscle-driven simulation system for generating biomechanically accurate motion for high-speed athletic locomotion tasks that does not require motion demonstrations. Ou…
Inference-Time Distillation: Cost-Efficient Agents Without Fine-Tuning or Manual Prompt Engineering
Vishnu Sarukkai, Asanshay Gupta, James Hong +2
Deploying LLM agents at scale typically requires choosing between quality and cost. Existing cost-reduction approaches fail to preserve agility: the ability to iterate rapidly with…
Bonsai: Compiling Queries to Pruned Tree Traversals
Alexander J Root, Christophe Gyurgyik, Purvi Goel +4
Trees can accelerate queries that search or aggregate values over large collections. They achieve this by storing metadata that enables quick pruning (or inclusion) of subtrees whe…
Learning to Ball: Composing Policies for Long-Horizon Basketball Moves
Pei Xu, Zhen Wu, Ruocheng Wang +5
Learning a control policy for a multi-phase, long-horizon task, such as basketball maneuvers, remains challenging for reinforcement learning approaches due to the need for seamless…
Generating Detailed Character Motion from Blocking Poses
Purvi Goel, Guy Tevet, C. K. Liu +1
We focus on the problem of using generative diffusion models for the task of motion detailing: converting a rough version of a character animation, represented by a sparse set of c…
Self-Generated In-Context Examples Improve LLM Agents for Sequential Decision-Making Tasks
Vishnu Sarukkai, Zhiqiang Xie, Kayvon Fatahalian
Improving Large Language Model (LLM) agents for sequential decision-making tasks typically requires extensive task-specific knowledge engineering--custom prompts, curated examples,…