qhjqhj00 robosuite-manipulation-eval
Waypoint-Based Reinforcement Learning for Robot Manipulation Tasks — Mehta et al. (2024) (arXiv:2403.13281, 2024)
What this evaluates
Evaluates the ability of reinforcement learning algorithms to learn sequential robot manipulation tasks in simulation. It probes how well methods can handle randomized initial states, multi-stage objectives, and continuous control over fixed-horizon episodes.
Datasets
- robosuite — total ?; splits: test (100)
Metrics
reward(primary) — range: other- Sum of per-step rewards over an episode: R(τ) = Σ_{s∈τ} r(s). Reported as average reward ± standard error across 100 evaluation episodes.
Input / output format
Input: World state s including robot arm configuration and randomized object positions/orientations at episode start.
Output: Low-level continuous actions a for baselines, or high-level waypoints s_R^t for waypoint methods, executed via a fixed impedance controller over 50-timestep intervals.
Scoring recipe
def evaluate(model, env, num_episodes=100, H=100):
rewards = []
for _ in range(num_episodes):
obs = env.reset(randomize=True)
episode_reward = 0
for _ in range(H):
action = model.predict(obs)
obs, reward, done, _ = env.step(action)
episode_reward += reward
rewards.append(episode_reward)
return mean(rewards), std(rewards)
Common pitfalls
- Reward functions for Stack, Nut Assembly, and Pick-and-Place were modified by the authors (increased stage rewards, added knock-over penalties), so results are not directly comparable to standard robosuite baselines.
- Initial object positions and orientations are randomized every episode, meaning performance must be averaged over many trials rather than reported on a fixed seed.
- Waypoint baselines (SAC-wp, PPO-wp) use a fixed impedance controller for 50 timesteps per waypoint, decoupling high-level planning from low-level control.
Evidence (verbatim from paper)
The robot’s task performance was measured using reward. If the robot encountered a trajectory of world state τ during a given episode, we reported: R(τ)=∑_{s∈τ}r(s). Higher rewards indicate better performance. ... Here the simulated robot attempted to complete each manipulation task 100 times using the models it had learned from training.
Citation
@misc{mehta2024waypoint,
title={Waypoint-Based Reinforcement Learning for Robot Manipulation Tasks},
author={Mehta et al. (2024)},
year={2024},
note={arXiv:2403.13281}
}
- arXiv: 2403.13281