メニュー
Enable robot policies to self-improve through imagination using learned dynamics and value models without physical trial-and-error. Compositional world model separates concerns enabling 35-45% performance gains on contact-rich manipulation.
デフォルトでは、最初にソースを確認する Prompt が選択されています。直接コマンドに切り替えるか、ローカルコピーをダウンロードすることもできます。
インストールを決める前に、SKILL.md と SkillsMP に表示されている付属ファイルをお読みください。
Codex または Claude でインストール この Prompt をコピーして Codex、Claude、または他のアシスタントに貼り付けると、Skill ページを確認してインストールできます。
直接コマンドでは確認用 Prompt が省略されます。実行前にソースを確認してください。
npx skills add https://github.com/ADu2021/skillXiv --skill rise-robotコマンドは1行のまま表示されます。コピー前に横へスクロールして全体を確認してください。
ローカルで確認しますか？SkillsMP が現在取得できるファイルをダウンロードできます。
Convert arXiv papers into ready-to-use agent skills using category-aware extraction. First classifies the paper into one or more of 11 research categories, then applies a specialized extraction pipeline for each category — because different types of papers produce different types of usable knowledge. A single paper can yield multiple skills if it spans categories. Use this skill whenever the user wants to turn a paper into a skill, extract practical techniques from research, build a skill library from papers, convert arXiv papers into reusable agent instructions, or batch-process multiple papers into skills. Also trigger when someone asks about extracting actionable knowledge from papers, making research practical for LLM agents, or systematically converting academic contributions into structured agent capabilities.
Establish regret bounds for behavior cloning with discretized actions combining statistical error and quantization error terms. Prove smoothness requirements for safe quantizer design, show that learning-based quantizers fail these requirements, and propose model-based augmentation to reduce error dependence from H² to H.
Dynamically allocate LoRA ranks per-layer during fine-tuning instead of using fixed uniform ranks. Learn optimal rank for each layer and subject via variational framework with discretized exponential distribution, reducing memory footprint while maintaining fidelity and text-alignment.
SOC 職業分類に基づく
SKILL.md を表示中
| name | rise-robot |
| title | RISE: Self-Improving Robot Policy with Compositional World Model |
| version | 0.0.2 |
| engine | skillxiv-v0.0.2-claude-opus-4.6 |
| license | MIT |
| url | https://arxiv.org/abs/2602.11075 |
| keywords | ["Robot Learning","World Models","Reinforcement Learning","Imagination","Policy Optimization","Compositional"] |
| description | Enable robot policies to self-improve through imagination using learned dynamics and value models without physical trial-and-error. Compositional world model separates concerns enabling 35-45% performance gains on contact-rich manipulation. |
Vision-Language-Action (VLA) foundation models excel at semantic understanding but struggle with precise contact-rich manipulation requiring fine motor control. Physical trial-and-error RL is expensive and impractical for real robots. Standard world models struggle to balance action controllability with realistic prediction. The core challenge: learn manipulation policies efficiently without costly physical interaction.
RISE shifts robot learning from the physical world to imagination using a compositional world model with specialized dynamics and value components. Rather than joint training, the approach separates:
This separation enables on-policy RL in imagination, where the policy iteratively improves by proposing actions, simulating consequences, evaluating outcomes, and updating based on advantage signals—all without touching a physical robot.
The compositional world model operates through two specialized components trained with different objectives:
class DynamicsModel(nn.Module):
"""Predicts future observations from current state and action."""
def __init__(self):
# Video diffusion backbone
self.backbone = VideoDiffusion()
def forward(self, observations, actions):
state_action = torch.cat([observations, actions], dim=)
predictions = .backbone.sample(state_action, steps=)
predictions
The value model combines progress and failure signals:
class ValueModel(nn.Module):
"""Evaluates imagined trajectories for advantage computation."""
def __init__(self, vla_backbone):
self.backbone = vla_backbone # Pre-trained VLA
self.progress_head = nn.Linear(hidden_dim, 1)
self.td_head = nn.Linear(hidden_dim, 1)
def forward(self, trajectory):
"""
Evaluate trajectory quality with dual objectives.
trajectory: sequence of observations
Returns: advantage signals for policy optimization
"""
features = self.backbone.encode(trajectory)
# Progress regression: how much closer to goal
progress = self.progress_head(features)
# Temporal-Difference learning: detect failures vs successes
td_values = self.td_head(features)
# Combine signals
return progress + td_values
Self-improving policy optimization loop:
def self_improving_loop(policy, dynamics_model, value_model,
goal_spec, num_iterations=100):
"""
Imagination-based RL loop without physical interaction.
"""
for iteration in range(num_iterations):
# Sample task from curriculum
task = sample_task(goal_spec)
# Generate action proposals with advantage conditioning
optimal_advantages = compute_target_advantages(task)
proposed_actions = policy.sample(
observations=task.initial_obs,
target_advantages=optimal_advantages
)
# Simulate in learned dynamics model
imagined_trajectory = []
obs = task.initial_obs
for t in range(horizon):
action = proposed_actions[t]
obs = dynamics_model(obs, action)
imagined_trajectory.append(obs)
# Evaluate imagined trajectory
values = value_model(imagined_trajectory)
# Compute advantage: difference between proposed and evaluated
advantages = optimal_advantages - values
# Policy optimization step
policy.update(task.initial_obs, proposed_actions, advantages)
yield {
'iteration': iteration,
'mean_advantage': advantages.mean().item(),
'policy_loss': policy.loss.item()
}
When to use: RISE is most effective for:
Key implementation steps:
Task-centric batching strategy:
Expected improvements:
Failure modes to avoid:
Compositional world models enable efficient robot learning by separating dynamics prediction and value estimation. The self-improving loop demonstrates how imagination-based RL can substantially improve policies without expensive physical trial-and-error on real robots.