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Found 9 papers, 2 papers with code
DeFT: Flash Tree-attention with IO-Awareness for Efficient Tree-search-based LLM Inference
Decoding using tree search can greatly enhance the inference quality for transformer-based Large Language Models (LLMs).
Behavioral Refinement via Interpolant-based Policy Diffusion
However, the target policy to be learned is often significantly different from Gaussian and this mismatch can result in poor performance when using a small number of diffusion steps (to improve inference speed) and under limited data.
Latent Emission-Augmented Perspective-Taking (LEAPT) for Human-Robot Interaction
Perspective-taking is the ability to perceive or understand a situation or concept from another individual's point of view, and is crucial in daily human interactions.
Semantic Visual Simultaneous Localization and Mapping: A Survey
Numerous publications have reported that, by combining with the semantic information with vSLAM, the semantic vSLAM systems have the capability of solving the above problems in recent years.
MIRROR: Differentiable Deep Social Projection for Assistive Human-Robot Communication
In this work, we present MIRROR, an approach to (i) quickly learn human models from human demonstrations, and (ii) use the models for subsequent communication planning in assistive shared-control settings.
Multi-Modal Mutual Information (MuMMI) Training for Robust Self-Supervised Deep Reinforcement Learning
This work focuses on learning useful and robust deep world models using multiple, possibly unreliable, sensors.
Collaborative Visual Inertial SLAM for Multiple Smart Phones
Each agent can independently explore the environment, run a visual-inertial odometry module online, and then send all the measurement information to a central server with higher computing resources.
Accurate Object Association and Pose Updating for Semantic SLAM
The key to solving this problem is to correctly associate multiple object measurements of one object landmark, and refine the pose of object landmark.
Improving Layer-wise Adaptive Rate Methods using Trust Ratio Clipping
Training neural networks with large batch is of fundamental significance to deep learning.
