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Found 9 papers, 8 papers with code
The RoboDepth Challenge: Methods and Advancements Towards Robust Depth Estimation
In this paper, we summarize the winning solutions from the RoboDepth Challenge -- an academic competition designed to facilitate and advance robust OoD depth estimation.
Model-free Motion Planning of Autonomous Agents for Complex Tasks in Partially Observable Environments
We formulate motion planning as a probabilistic-labeled partially observable Markov decision process (PL-POMDP) problem and use linear temporal logic (LTL) to express the complex task.
Exploiting Transformer in Sparse Reward Reinforcement Learning for Interpretable Temporal Logic Motion Planning
Automaton based approaches have enabled robots to perform various complex tasks.
TODE-Trans: Transparent Object Depth Estimation with Transformer
We observe that the global characteristics of the transformer make it easier to extract contextual information to perform depth estimation of transparent areas.
A Robotic Visual Grasping Design: Rethinking Convolution Neural Network with High-Resolutions
We demonstrate that using parallel branches as opposed to serial stacked convolutional layers will be a more powerful design for robotic visual grasping tasks.
When Transformer Meets Robotic Grasping: Exploits Context for Efficient Grasp Detection
The first key design is that we adopt the local window attention to capture local contextual information and detailed features of graspable objects.
Modular Deep Reinforcement Learning for Continuous Motion Planning with Temporal Logic
This paper investigates the motion planning of autonomous dynamical systems modeled by Markov decision processes (MDP) with unknown transition probabilities over continuous state and action spaces.
Reinforcement Learning Based Temporal Logic Control with Soft Constraints Using Limit-deterministic Generalized Buchi Automata
This paper studies the control synthesis of motion planning subject to uncertainties.
Reinforcement Learning Based Temporal Logic Control with Maximum Probabilistic Satisfaction
This paper presents a model-free reinforcement learning (RL) algorithm to synthesize a control policy that maximizes the satisfaction probability of linear temporal logic (LTL) specifications.
