RECORD
Triple Regression for Camera Agnostic Sim2Real Robot Grasping and Manipulation Tasks
Robotics · Submitted 16 Sep 2023
abstract compiled · 2026-09-23 · no language-model review
The authors' main claim Remarkably, with only RGB input images, our method achieves state-of-the-art success rates.
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The abstract, sentence by sentence
Sim2Real (Simulation to Reality) techniques have gained prominence in robotic manipulation and motion planning due to their ability to enhance success rates by enabling agents to test and evaluate various policies and trajectories. In this paper, we investigate the advantages of integrating Sim2Real into robotic frameworks. We introduce the Triple Regression Sim2Real framework, which constructs a real-time digital twin. This twin serves as a replica of reality to simulate and evaluate multiple plans before their execution in real-world scenarios. Our triple regression approach addresses the reality gap by: (1) mitigating projection errors between real and simulated camera perspectives through the first two regression models, and (2) detecting discrepancies in robot control using the third regression model. Experiments on 6-DoF grasp and manipulation tasks (where the gripper can approach from any direction) highlight the effectiveness of our framework. Remarkably, with only RGB input images, our method achieves state-of-the-art success rates. This research advances efficient robot training methods and sets the stage for rapid advancements in robotics and automation.
background contribution method result interpretation main claim
| # | sentence | role | the authors' own work | states the main conclusion | strength |
|---|---|---|---|---|---|
| s0 | Sim2Real (Simulation to Reality) techniques have gained prominence in robotic manipulation and motion planning due to their ability to enhance success rates by enabling agents to test and evaluate various policies and trajectories. | background 1.00 | 0.05 | 0.03 | asserted 0.99 |
| s1 | In this paper, we investigate the advantages of integrating Sim2Real into robotic frameworks. | contribution 0.72 | 0.58 | 0.03 | unclear 0.71 |
| s2 | We introduce the Triple Regression Sim2Real framework, which constructs a real-time digital twin. | contribution 0.92 | 0.92 | 0.06 | asserted 1.00 |
| s3 | This twin serves as a replica of reality to simulate and evaluate multiple plans before their execution in real-world scenarios. | method 0.88 | 0.38 | 0.05 | asserted 1.00 |
| s4 | Our triple regression approach addresses the reality gap by: (1) mitigating projection errors between real and simulated camera perspectives through the first two regression models, and (2) detecting discrepancies in robot control using the third regression model. | method 0.96 | 0.83 | 0.10 | asserted 1.00 |
| s5 | Experiments on 6-DoF grasp and manipulation tasks (where the gripper can approach from any direction) highlight the effectiveness of our framework. | method 0.55 | 0.93 | 0.11 | asserted 1.00 |
| s6 | Remarkably, with only RGB input images, our method achieves state-of-the-art success rates. | result 1.00 | 0.94 | 0.42 | asserted 1.00 |
| s7 | This research advances efficient robot training methods and sets the stage for rapid advancements in robotics and automation. | interpretation 0.94 | 0.17 | 0.22 | asserted 0.93 |
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The paper
Original abstract
Sim2Real (Simulation to Reality) techniques have gained prominence in robotic manipulation and motion planning due to their ability to enhance success rates by enabling agents to test and evaluate various policies and trajectories. In this paper, we investigate the advantages of integrating Sim2Real into robotic frameworks. We introduce the Triple Regression Sim2Real framework, which constructs a real-time digital twin. This twin serves as a replica of reality to simulate and evaluate multiple plans before their execution in real-world scenarios. Our triple regression approach addresses the reality gap by: (1) mitigating projection errors between real and simulated camera perspectives through the first two regression models, and (2) detecting discrepancies in robot control using the third regression model. Experiments on 6-DoF grasp and manipulation tasks (where the gripper can approach from any direction) highlight the effectiveness of our framework. Remarkably, with only RGB input images, our method achieves state-of-the-art success rates. This research advances efficient robot training methods and sets the stage for rapid advancements in robotics and automation.
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What this paper does with the arXiv papers it cites
What it cites · 1 sentence without a confident label
Uses the method or tool of 3 papers
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Contour lines of the objects are extracted using [ref] and key points are subsequently determined for cuboid and cylindrical objects.
Segment Anything · Triple Regression for Matching Simulation and Reality · 1.00
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RMPflow was employed [ref] to generate smooth policies to control the robot both in the real world and in simulation.
Riemannian Motion Policies · Grasp and pour experiment · 1.00
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Anchor points and bounding boxes are generated with pattern matching or text-to-image grounding models [ref] .
Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detection · Triple Regression for Matching Simulation and Reality · 0.98
Cites as background 1 paper
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On another model MiniGPT-4 [ref] emerges as a compact yet potent architecture consisting of the vision transform and large language models, showcasing impressive text generation prowess within a more resource-efficient framework.
MiniGPT-4: Enhancing Vision-Language Understanding with Advanced Large Language Models · Comparison of VQA models · 0.93
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