Taming the Panda with Python: A Powerful Duo for Seamless Robotics Programming and Integration
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Abstract (arXiv)
Franka Emika robots have gained significant popularity in research and education due to their exceptional versatility and advanced capabilities. This work introduces panda-py - a Python interface and framework designed to empower Franka Emika robotics with accessible and efficient programming. The panda-py interface enhances the usability of Franka Emika robots, enabling researchers and educators to interact with them more effectively. By leveraging Python's simplicity and readability, users can quickly grasp the necessary programming concepts for robot control and manipulation. Moreover, integrating panda-py with other widely used Python packages in domains such as computer vision and machine learning amplifies the robot's capabilities. Researchers can seamlessly leverage the vast ecosystem of Python libraries, thereby enabling advanced perception, decision-making, and control functionalities. This compatibility facilitates the efficient development of sophisticated robotic applications, integrating state-of-the-art techniques from diverse domains without the added complexity of ROS.
Key facts. Cited by 10 papers in the map (data through 13 Oct 2026). First posted on arXiv 14 Jul 2023.
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Citing papers per year: 2024: 2; 2025: 6; 2026: 2.
10 papers.
- 2024MALMM: Multi-Agent Large Language Models for Zero-Shot Robotics Manipulationcited by 18
- 2025Robot Control Stack: A Lean Ecosystem for Robot Learning at Scalecited by 8
- 2025Toward Artificial Palpation: Representation Learning of Touch on Soft Bodiescited by 1
- 2024CoBOS: Constraint-Based Online Scheduler for Human-Robot Collaborationcited by 0
- 2025Robot Policy Transfer with Online Demonstrations: An Active Reinforcement Learning Approachcited by 0
- 2025What Do You Need for Compositional Generalization in Diffusion Planning?cited by 0
- 2025DeGuV: Depth-Guided Visual Reinforcement Learning for Generalization and Interpretability in Manipulationcited by 0
- 2025BLAZER: Bootstrapping LLM-based Manipulation Agents with Zero-Shot Data Generationcited by 0
- 2026Minimal Embodiment Enables Efficient Learning of Number Concepts in Robotcited by 0
- 2026More with LESS -- Local Scene Representations for Tactile Imagingcited by 0
- 2026More with LESS -- Local Scene Representations for Tactile Imaging12 Jun 2026
- 2026Minimal Embodiment Enables Efficient Learning of Number Concepts in Robot13 Apr 2026
- 2025Toward Artificial Palpation: Representation Learning of Touch on Soft Bodies20 Nov 2025
- 2025BLAZER: Bootstrapping LLM-based Manipulation Agents with Zero-Shot Data Generation9 Oct 2025
- 2025Robot Control Stack: A Lean Ecosystem for Robot Learning at Scale18 Sep 2025
- 2025DeGuV: Depth-Guided Visual Reinforcement Learning for Generalization and Interpretability in Manipulation5 Sep 2025
- 2025What Do You Need for Compositional Generalization in Diffusion Planning?23 May 2025
- 2025Robot Policy Transfer with Online Demonstrations: An Active Reinforcement Learning Approach17 Mar 2025
- 2024MALMM: Multi-Agent Large Language Models for Zero-Shot Robotics Manipulation26 Nov 2024
- 2024CoBOS: Constraint-Based Online Scheduler for Human-Robot Collaboration27 Mar 2024