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Works18 from public data
- ALPHA: Attention-based Long-horizon Pathfinding in Highly-structured Areas19
This work proposes ALPHA, a new framework combining the use of ground truth proximal (local) information and fuzzy distal (global) information to let agents sequence local decisions based on the full current state of the system, and avoid such myopicity.
- Demystifying Diffusion Policies: Action Memorization and Simple Lookup Table Alternatives18
This paper proposes a simple policy, the Action Lookup Table (ALT), as a lightweight alternative to the Diffusion Policy, and shows empirically that for relatively small datasets, ALT matches the performance of a diffusion model, while requiring only 0.0034 of the inference time and 0.0085 of the memory footprint.
- Latent Theory of Mind: A Decentralized Diffusion Architecture for Cooperative Manipulation17
The Latent Theory of Mind, a decentralized diffusion policy architecture for collaborative robot manipulation, is presented, allowing each agent to maintain two latent representations: an ego embedding specific to the robot, and a consensus embedding trained to be common to both robots, despite their different sensor streams and poses.
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- SIGMA: Sheaf-Informed Geometric Multi-Agent Pathfinding8
A new framework that applies sheaf theory to decentralized deep reinforcement learning is introduced, enabling agents to learn geometric cross-dependencies between each other through local consensus and utilize them for tightly cooperative decision-making in Multi-Agent Path Finding.
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- Hybrid Training for Enhanced Multi-task Generalization in Multi-agent Reinforcement Learning4
This paper introduces HyGen, a novel hybrid MARL framework, Hybrid Training for Enhanced Multi-Task Generalization, which integrates online and offline learning to ensure both multi-task generalization and training efficiency.
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- TBRNet: A Multi-Modal Network for Teacher Behavior Recognition with Cascaded Collaborative Attention and Dynamic Query-Driven1
A multi-modal network, TBRNet, aiming to improve recognition performance and facilitate teaching reflection is proposed, which provides an effective method for efficiently identifying teacher teaching behaviors in real classroom environments.
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- X-Morph: Human Motion Priors for Scalable Robot Learning Across Morphologies–
X-Morph is presented, a human-motion-to-robot-behavior pipeline that converts human motion into deployable locomotion and loco-manipulation policies for diverse non-humanoid legged morphologies and suggests that large-scale human motion can serve as a substrate for learning broad, reusable behavior priors beyond humanoid robots.
- Social Behavior as a Key to Learning-Based Multi-Agent Pathfinding Dilemmas (Abstract Reprint)–
SYLPH is proposed, a novel learning-based MAPF framework aimed to mitigate the adverse effects of homogeneity by allowing agents to learn and dynamically select different social behaviors (akin to individual, dynamic roles), without affecting the scalability offered by parameter sharing.
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Publication data from OpenAlex; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-11. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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