Robosuite Manipulation Eval

---
name: robosuite-manipulation-eval
description: 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. Use when the user wants to benchmark on robosuite, or asks about evaluating this task. Reports reward.
metadata:
  skill_kind: dataset_eval
  source_arxiv: 2403.13281
  bibtex_key: [REDACTED]
  confidence: high
---

# 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

```python
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

```bibtex
@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