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Perception in Plan: Coupled Perception and Planning for End-to-End Autonomous Driving
End-to-end autonomous driving has achieved remarkable advancements in recent years. Existing methods primarily follow a perception-planning paradigm, where perception and planning are executed sequentially within a fully differentiable framework for planning-oriented optimization. We further advance this paradigm throu...
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zrz@andrew.cmu.edu
Perception in Plan: Coupled Perception and Planning for End-to-End Autonomous Driving : End-to-end autonomous driving has achieved remarkable advancements in recent years. Existing methods primarily follow a perception-planning paradigm, where perception and planning are executed sequentially within a fully differentia...
1
zrz@andrew.cmu.edu [SEP] Perception in Plan: Coupled Perception and Planning for End-to-End Autonomous Driving : End-to-end autonomous driving has achieved remarkable advancements in recent years. Existing methods primarily follow a perception-planning paradigm, where perception and planning are executed sequentially w...
271
ETA-IK: Execution-Time-Aware Inverse Kinematics for Dual-Arm Systems
This paper presents ETA-IK, a novel Execution-Time-Aware Inverse Kinematics method tailored for dual-arm robotic systems. The primary goal is to optimize motion execution time by leveraging the redundancy of both arms, specifically in tasks where only the relative pose of the robots is constrained, such as dual-arm sca...
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jechoi@andrew.cmu.edu
ETA-IK: Execution-Time-Aware Inverse Kinematics for Dual-Arm Systems : This paper presents ETA-IK, a novel Execution-Time-Aware Inverse Kinematics method tailored for dual-arm robotic systems. The primary goal is to optimize motion execution time by leveraging the redundancy of both arms, specifically in tasks where on...
1
jechoi@andrew.cmu.edu [SEP] ETA-IK: Execution-Time-Aware Inverse Kinematics for Dual-Arm Systems : This paper presents ETA-IK, a novel Execution-Time-Aware Inverse Kinematics method tailored for dual-arm robotic systems. The primary goal is to optimize motion execution time by leveraging the redundancy of both arms, sp...
539
Transformer-based deep imitation learning for dual-arm robot manipulation
Deep imitation learning is promising for solving dexterous manipulation tasks because it does not require an environment model and pre-programmed robot behavior. However, its application to dual-arm manipulation tasks remains challenging. In a dual-arm manipulation setup, the increased number of state dimensions caused...
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jechoi@andrew.cmu.edu
Transformer-based deep imitation learning for dual-arm robot manipulation : Deep imitation learning is promising for solving dexterous manipulation tasks because it does not require an environment model and pre-programmed robot behavior. However, its application to dual-arm manipulation tasks remains challenging. In a ...
1
jechoi@andrew.cmu.edu [SEP] Transformer-based deep imitation learning for dual-arm robot manipulation : Deep imitation learning is promising for solving dexterous manipulation tasks because it does not require an environment model and pre-programmed robot behavior. However, its application to dual-arm manipulation task...
464
Validation of a Control Algorithm for Human-like Reaching Motion using 7-DOF Arm and 19-DOF Hand-Arm Systems
This technical report gives an overview of our work on control algorithms dealing with redundant robot systems for achieving human-like motion characteristics. Previously, we developed a novel control law to exhibit human-motion characteristics in redundant robot arm systems as well as arm-trunk systems for reaching ta...
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jechoi@andrew.cmu.edu
Validation of a Control Algorithm for Human-like Reaching Motion using 7-DOF Arm and 19-DOF Hand-Arm Systems : This technical report gives an overview of our work on control algorithms dealing with redundant robot systems for achieving human-like motion characteristics. Previously, we developed a novel control law to e...
0
jechoi@andrew.cmu.edu [SEP] Validation of a Control Algorithm for Human-like Reaching Motion using 7-DOF Arm and 19-DOF Hand-Arm Systems : This technical report gives an overview of our work on control algorithms dealing with redundant robot systems for achieving human-like motion characteristics. Previously, we develo...
481
Using Deep Learning and Machine Learning to Detect Epileptic Seizure with Electroencephalography (EEG) Data
The prediction of epileptic seizure has always been extremely challenging in medical domain. However, as the development of computer technology, the application of machine learning introduced new ideas for seizure forecasting. Applying machine learning model onto the predication of epileptic seizure could help us obtai...
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zrz@andrew.cmu.edu
Using Deep Learning and Machine Learning to Detect Epileptic Seizure with Electroencephalography (EEG) Data : The prediction of epileptic seizure has always been extremely challenging in medical domain. However, as the development of computer technology, the application of machine learning introduced new ideas for seiz...
1
zrz@andrew.cmu.edu [SEP] Using Deep Learning and Machine Learning to Detect Epileptic Seizure with Electroencephalography (EEG) Data : The prediction of epileptic seizure has always been extremely challenging in medical domain. However, as the development of computer technology, the application of machine learning intr...
102
Learning Multi-Arm Manipulation Through Collaborative Teleoperation
Imitation Learning (IL) is a powerful paradigm to teach robots to perform manipulation tasks by allowing them to learn from human demonstrations collected via teleoperation, but has mostly been limited to single-arm manipulation. However, many real-world tasks require multiple arms, such as lifting a heavy object or as...
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jechoi@andrew.cmu.edu
Learning Multi-Arm Manipulation Through Collaborative Teleoperation : Imitation Learning (IL) is a powerful paradigm to teach robots to perform manipulation tasks by allowing them to learn from human demonstrations collected via teleoperation, but has mostly been limited to single-arm manipulation. However, many real-w...
0
jechoi@andrew.cmu.edu [SEP] Learning Multi-Arm Manipulation Through Collaborative Teleoperation : Imitation Learning (IL) is a powerful paradigm to teach robots to perform manipulation tasks by allowing them to learn from human demonstrations collected via teleoperation, but has mostly been limited to single-arm manipu...
22
Automated Graph Machine Learning: Approaches, Libraries, Benchmarks and Directions
Graph machine learning has been extensively studied in both academic and industry. However, as the literature on graph learning booms with a vast number of emerging methods and techniques, it becomes increasingly difficult to manually design the optimal machine learning algorithm for different graph-related tasks. To t...
Liked
zrz@andrew.cmu.edu
Automated Graph Machine Learning: Approaches, Libraries, Benchmarks and Directions : Graph machine learning has been extensively studied in both academic and industry. However, as the literature on graph learning booms with a vast number of emerging methods and techniques, it becomes increasingly difficult to manually ...
1
zrz@andrew.cmu.edu [SEP] Automated Graph Machine Learning: Approaches, Libraries, Benchmarks and Directions : Graph machine learning has been extensively studied in both academic and industry. However, as the literature on graph learning booms with a vast number of emerging methods and techniques, it becomes increasing...
139
Automatic Design of Task-specific Robotic Arms
We present an interactive, computational design system for creating custom robotic arms given high-level task descriptions and environmental constraints. Various task requirements can be encoded as desired motion trajectories for the robot arm's end-effector. Given such end-effector trajectories, our system enables on-...
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jechoi@andrew.cmu.edu
Automatic Design of Task-specific Robotic Arms : We present an interactive, computational design system for creating custom robotic arms given high-level task descriptions and environmental constraints. Various task requirements can be encoded as desired motion trajectories for the robot arm's end-effector. Given such ...
1
jechoi@andrew.cmu.edu [SEP] Automatic Design of Task-specific Robotic Arms : We present an interactive, computational design system for creating custom robotic arms given high-level task descriptions and environmental constraints. Various task requirements can be encoded as desired motion trajectories for the robot arm...
7
Spatial Transfer Learning with Simple MLP
First step to investigate the potential of transfer learning applied to the field of spatial statistics
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zrz@andrew.cmu.edu
Spatial Transfer Learning with Simple MLP : First step to investigate the potential of transfer learning applied to the field of spatial statistics
0
zrz@andrew.cmu.edu [SEP] Spatial Transfer Learning with Simple MLP : First step to investigate the potential of transfer learning applied to the field of spatial statistics
57
Poisoning Deep Reinforcement Learning Agents with In-Distribution Triggers
In this paper, we propose a new data poisoning attack and apply it to deep reinforcement learning agents. Our attack centers on what we call in-distribution triggers, which are triggers native to the data distributions the model will be trained on and deployed in. We outline a simple procedure for embedding these, and ...
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zrz@andrew.cmu.edu
Poisoning Deep Reinforcement Learning Agents with In-Distribution Triggers : In this paper, we propose a new data poisoning attack and apply it to deep reinforcement learning agents. Our attack centers on what we call in-distribution triggers, which are triggers native to the data distributions the model will be traine...
1
zrz@andrew.cmu.edu [SEP] Poisoning Deep Reinforcement Learning Agents with In-Distribution Triggers : In this paper, we propose a new data poisoning attack and apply it to deep reinforcement learning agents. Our attack centers on what we call in-distribution triggers, which are triggers native to the data distributions...
258
The ATLAS of Traffic Lights: A Reliable Perception Framework for Autonomous Driving
Traffic light perception is an essential component of the camera-based perception system for autonomous vehicles, enabling accurate detection and interpretation of traffic lights to ensure safe navigation through complex urban environments. In this work, we propose a modularized perception framework that integrates sta...
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zrz@andrew.cmu.edu
The ATLAS of Traffic Lights: A Reliable Perception Framework for Autonomous Driving : Traffic light perception is an essential component of the camera-based perception system for autonomous vehicles, enabling accurate detection and interpretation of traffic lights to ensure safe navigation through complex urban environ...
0
zrz@andrew.cmu.edu [SEP] The ATLAS of Traffic Lights: A Reliable Perception Framework for Autonomous Driving : Traffic light perception is an essential component of the camera-based perception system for autonomous vehicles, enabling accurate detection and interpretation of traffic lights to ensure safe navigation thro...
327
Empirical Investigation of Factors that Influence Human Presence and Agency in Telepresence Robot
Nowadays, a community starts to find the need for human presence in an alternative way, there has been tremendous research and development in advancing telepresence robots. People tend to feel closer and more comfortable with telepresence robots as many senses a human presence in robots. In general, many people feel th...
Liked
jechoi@andrew.cmu.edu
Empirical Investigation of Factors that Influence Human Presence and Agency in Telepresence Robot : Nowadays, a community starts to find the need for human presence in an alternative way, there has been tremendous research and development in advancing telepresence robots. People tend to feel closer and more comfortable...
1
jechoi@andrew.cmu.edu [SEP] Empirical Investigation of Factors that Influence Human Presence and Agency in Telepresence Robot : Nowadays, a community starts to find the need for human presence in an alternative way, there has been tremendous research and development in advancing telepresence robots. People tend to feel...
459
Development of a Feeding Assistive Robot Using a Six Degree of Freedom Robotic Arm
This project introduces a Feeding Assistive Robot tailored to individuals with physical disabilities, including those with limited arm function or hand control. The core component is a precise 6-degree freedom robotic arm, operated seamlessly through voice commands. Integration of an Arduino-based Braccio Arm, a distan...
Liked
jechoi@andrew.cmu.edu
Development of a Feeding Assistive Robot Using a Six Degree of Freedom Robotic Arm : This project introduces a Feeding Assistive Robot tailored to individuals with physical disabilities, including those with limited arm function or hand control. The core component is a precise 6-degree freedom robotic arm, operated sea...
1
jechoi@andrew.cmu.edu [SEP] Development of a Feeding Assistive Robot Using a Six Degree of Freedom Robotic Arm : This project introduces a Feeding Assistive Robot tailored to individuals with physical disabilities, including those with limited arm function or hand control. The core component is a precise 6-degree freed...
411