Learning to Throw Objects Safely in Multi-Obstacle Environments

2026-07-08 · Source: cs.CV updates on arXiv.org · Field: Technology & Digital — Robotics & Autonomous Systems, Artificial Intelligence & Machine Learning · Depth: Expert, extended

Summary

Researchers have developed a novel potential field state representation (PFR) for robotic throwing, enabling robots to safely toss objects into target baskets while avoiding multiple, randomly placed obstacles. This approach, which uses kinesthetic teaching for safe policy initialization, was evaluated against an Explicit Pose Representation (EPR) and three reinforcement learning algorithms: SAC, DDPG, and TD3. PFR compactly encodes basket attraction and obstacle repulsion on a fixed-size 15x15 grid, allowing policies to generalize across arbitrary obstacle numbers and configurations. In contrast, EPR's dimensionality scales with obstacle count. Simulation and real-robot experiments demonstrated that SAC consistently achieved the most robust performance, with PFR policies reaching up to 90% success rates in cluttered real-world scenes, even with unseen throwable objects like a sneaker. This method offers a scalable and robust solution for efficient object placement beyond a robot's immediate workspace.

Key takeaway

For robotics engineers deploying automated throwing systems in cluttered environments, you should prioritize state representations that scale efficiently with obstacle count. The Potential Field Representation (PFR) offers a robust solution, enabling a single policy to generalize across varying obstacle configurations. You can enhance training safety and efficiency by initializing reinforcement learning policies with kinesthetic teaching. Furthermore, consider Soft Actor-Critic (SAC) as the preferred algorithm for its consistent performance and strong sim-to-real transferability, achieving up to 90% success in real-world scenarios.

Key insights

Potential Field Representation (PFR) enables scalable, safe robotic throwing in cluttered environments by encoding obstacles and targets compactly.

Principles

• Potential fields offer scalable state encoding.
• Kinesthetic teaching provides safe RL initialization.
• SAC consistently outperforms DDPG and TD3.

Method

Robotic throwing is formulated as a safe RL task. Kinesthetic teaching initializes a throwing kernel, which RL policies (SAC, DDPG, TD3) then modulate using a potential field state representation.

In practice

• Use PFR for multi-obstacle robotic throwing.
• Initialize RL policies with kinesthetic teaching.
• Consider SAC for robust throwing policy learning.

Topics

• Robotic Throwing
• Potential Field Representation
• Reinforcement Learning
• Sim-to-Real Transfer
• Multi-Obstacle Navigation
• Kinesthetic Teaching

Best for: Research Scientist, AI Scientist, Robotics Engineer, Machine Learning Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.CV updates on arXiv.org.