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Found 12 papers, 4 papers with code
LLM-State: Open World State Representation for Long-horizon Task Planning with Large Language Model
We propose an open state representation that provides continuous expansion and updating of object attributes from the LLM's inherent capabilities for context understanding and historical action reasoning.
Technical Report for Argoverse Challenges on 4D Occupancy Forecasting
This report presents our Le3DE2E_Occ solution for 4D Occupancy Forecasting in Argoverse Challenges at CVPR 2023 Workshop on Autonomous Driving (WAD).
Technical Report for Argoverse Challenges on Unified Sensor-based Detection, Tracking, and Forecasting
We achieved 1st place in Detection, Tracking, and Forecasting on the E2E Forecasting track in Argoverse Challenges at CVPR 2023 WAD.
DiffMimic: Efficient Motion Mimicking with Differentiable Physics
Motion mimicking is a foundational task in physics-based character animation.
Imitation Learning As State Matching via Differentiable Physics
With the physics prior, ILD policies can not only be transferable to unseen environment specifications but also yield higher final performance on a variety of tasks.
Imitation Learning via Differentiable Physics
With the physics prior, ILD policies can not only be transferable to unseen environment specifications but also yield higher final performance on a variety of tasks.
Ab Initio Particle-based Object Manipulation
Like the model-based analytic approaches to manipulation, the particle representation enables the robot to reason about the object's geometry and dynamics in order to choose suitable manipulation actions.
Reconstructing 3D Model from Single-View Sketch with Deep Neural Network
It creates the transformation representation of a sketch by extracting the shape features of an input sketch and transformation template samples.
Improving Layer-wise Adaptive Rate Methods using Trust Ratio Clipping
Training neural networks with large batch is of fundamental significance to deep learning.
Contrastive Variational Reinforcement Learning for Complex Observations
This paper presents Contrastive Variational Reinforcement Learning (CVRL), a model-based method that tackles complex visual observations in DRL.
DinerDash Gym: A Benchmark for Policy Learning in High-Dimensional Action Space
It has been arduous to assess the progress of a policy learning algorithm in the domain of hierarchical task with high dimensional action space due to the lack of a commonly accepted benchmark.
