Sim-To-Real via Sim-To-Sim: Data-Efficient Robotic Grasping via Randomized-To-Canonical Adaptation Networks

Cited by (421)

Items 301–400 of 421

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29. What Do We Learn from a Large-Scale Study of Pre-Trained Visual Representations in Sim and Real Environments?
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31. Self-Supervised Learning of Grasping Arbitrary Objects On-the-Move
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32. Achieving time-critical control in Edge Robotics Systems over 5G
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36. Active Cross-domain Self-localization Using Pole-like Landmarks
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37. On Assessing the Usefulness of Proxy Domains for Developing and Evaluating Embodied Agents
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38. Reduced Simulation: Real-to-Sim Approach toward Collision Detection in Narrowly Confined Environments
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39. Improving the Environmental Adaptability of Conditional Imitation Learning Driving Model
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40. Multi-task Deep Reinforcement Learning with Evolutionary Algorithm and Policy Gradients Method in 3D Control Tasks
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44. "What, not how": Solving an under-actuated insertion task from scratch
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45. ERMAS: Becoming Robust to Reward Function Sim-to-Real Gaps in Multi-Agent Simulations.
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46. RL-DARTS: Differentiable Architecture Search for Reinforcement Learning.
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47. Learning Provably Robust Motion Planners Using Funnel Libraries
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48. An Affective Computing Multilayer Cognitive Architecture for Evolutionary Cognitive Robotics
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49. Shared control and authority distribution for robotic teleoperation
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50. Adversarial Imitation Learning between Agents with Different Numbers of State Dimensions
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51. Development of a Basic Educational Kit for Robot Development Using Deep Neural Networks
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52. Domain Adaptation with Morphologic Segmentation
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53. Robotic Visual Functions Required for Picking Tasks
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54. Deep Reinforcement Learning for Tactile Robotics: Learning to Type on a Braille Keyboard
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55. Nonprehensile Riemannian Motion Predictive Control
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56. Introspective Visuomotor Control: Exploiting Uncertainty in Deep Visuomotor Control for Failure Recovery
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60. Cycle-Consistent World Models for Domain Independent Latent Imagination
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62. 6d Object Pose Estimation with Location-and-Channel Attention
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