TacTip gym environment
TacTip-gym
TacTip-gym is a reinforcement learning environment designed for training and evaluating
agents using vision-based tactile sensing with the TacTip sensor.
It extends standard RL frameworks such as OpenAI Gym / Gymnasium
to support tactile perception tasks in robotics.
What is TacTip?
The TacTip is a biomimetic optical tactile sensor that uses a gel-like surface and internal markers tracked by a camera to measure deformation. This allows robots to "feel" contact, edges, texture, and shear forces in a way similar to human touch.
Key Features
- Gym-style API for reinforcement learning compatibility
- Simulated tactile observations based on TacTip sensor physics
- Useful for sim-to-real tactile robotics research
- Designed for benchmarking tactile perception and control tasks
Typical Use Cases
- Edge and surface following with tactile feedback
- Object manipulation using touch perception
- Learning contact-rich policies for soft robotics
- Comparing vision vs tactile-based control strategies
Environment Structure
The environment follows the standard Gym interface:
obs = env.reset()
action = policy(obs)
obs, reward, done, info = env.step(action)
Why TacTip-gym?
Traditional RL environments rely on visual or state-based inputs. TacTip-gym introduces high-dimensional tactile observations, enabling research into:
- Contact-driven learning
- Robust manipulation under uncertainty
- Sensorimotor learning from touch
This project is ideal for researchers working in robotics, reinforcement learning, and tactile perception who want to explore how machines can learn from touch.