Search Results for author:
Found 19 papers, 5 papers with code
Visual Whole-Body Control for Legged Loco-Manipulation
That is, the robot can control the legs and the arm at the same time to extend its workspace.
Retrieval is Accurate Generation
To address this, we propose to initialize the training oracles using linguistic heuristics and, more importantly, bootstrap the oracles through iterative self-reinforcement.
Expressive Whole-Body Control for Humanoid Robots
Can we enable humanoid robots to generate rich, diverse, and expressive motions in the real world?
Embracing Language Inclusivity and Diversity in CLIP through Continual Language Learning
To alleviate CF raised by covariate shift and lexical overlap, we further propose a novel approach that ensures the identical distribution of all token embeddings during initialization and regularizes token embedding learning during training.
ML-LMCL: Mutual Learning and Large-Margin Contrastive Learning for Improving ASR Robustness in Spoken Language Understanding
Specifically, in fine-tuning, we apply mutual learning and train two SLU models on the manual transcripts and the ASR transcripts, respectively, aiming to iteratively share knowledge between these two models.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+4
Qilin-Med: Multi-stage Knowledge Injection Advanced Medical Large Language Model
In the CPT and SFT phases, Qilin-Med achieved 38. 4% and 40. 0% accuracy on the CMExam test set, respectively.
Extreme Parkour with Legged Robots
In this paper, we take a similar approach to developing robot parkour on a small low-cost robot with imprecise actuation and a single front-facing depth camera for perception which is low-frequency, jittery, and prone to artifacts.
G2L: Semantically Aligned and Uniform Video Grounding via Geodesic and Game Theory
Due to two annoying issues in video grounding: (1) the co-existence of some visual entities in both ground truth and other moments, \ie semantic overlapping; (2) only a few moments in the video are annotated, \ie sparse annotation dilemma, vanilla contrastive learning is unable to model the correlations between temporally distant moments and learned inconsistent video representations.
PolyVoice: Language Models for Speech to Speech Translation
For the speech synthesis part, we adopt the existing VALL-E X approach and build a unit-based audio language model.
Unify, Align and Refine: Multi-Level Semantic Alignment for Radiology Report Generation
Automatic radiology report generation has attracted enormous research interest due to its practical value in reducing the workload of radiologists.
Legs as Manipulator: Pushing Quadrupedal Agility Beyond Locomotion
Locomotion has seen dramatic progress for walking or running across challenging terrains.
PoseRAC: Pose Saliency Transformer for Repetitive Action Counting
Moreover, we introduce a pose-level method, PoseRAC, which is based on this representation and achieves state-of-the-art performance on two new version datasets by using Pose Saliency Annotation to annotate salient poses for training.
Ranked #1 on
Repetitive Action Counting
on RepCount
Exploiting Auxiliary Caption for Video Grounding
Video grounding aims to locate a moment of interest matching the given query sentence from an untrimmed video.
M3ST: Mix at Three Levels for Speech Translation
How to solve the data scarcity problem for end-to-end speech-to-text translation (ST)?
A Dynamic Graph Interactive Framework with Label-Semantic Injection for Spoken Language Understanding
Multi-intent detection and slot filling joint models are gaining increasing traction since they are closer to complicated real-world scenarios.
Deep Whole-Body Control: Learning a Unified Policy for Manipulation and Locomotion
The standard hierarchical control pipeline for such legged manipulators is to decouple the controller into that of manipulation and locomotion.
Reinforcement Learning for Robust Parameterized Locomotion Control of Bipedal Robots
Developing robust walking controllers for bipedal robots is a challenging endeavor.
Automated Lane Change Strategy using Proximal Policy Optimization-based Deep Reinforcement Learning
The simulation results demonstrate the lane change maneuvers can be efficiently learned and executed in a safe, smooth, and efficient manner.
Driving Decision and Control for Autonomous Lane Change based on Deep Reinforcement Learning
We apply Deep Q-network (DQN) with the consideration of safety during the task for deciding whether to conduct the maneuver.
