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Show HN: I gave my robot physical memory – it stopped repeating mistakes
- DANmode 7mo agoRecommend providing a text summary of the comparison chart - and talking a bit about the API.
- robotmem 7mo agoThanks for the feedback! Results summary: Baseline heuristic policy achieves 42% success rate on FetchPush-v4. With memory augmentation (recall past experiences before each episode), it reaches 67% — a +25pp improvement. Cross-environment transfer from FetchPush to FetchSlide adds +8pp over baseline. The API has 7 endpoints — the core loop is: - learn(insight, context) — store what worked (or failed) - recall(query) — retrieve relevant past experiences, ranked by text + vector + spatial similarity - save_perception(data) — store raw trajectories/forces - start_session / end_session — episode lifecycle with auto-consolidation Everything runs on SQLite locally. No cloud, no GPU. Works via MCP (Model Context Protocol) or direct Python import. pip install robotmem — quick demo runs in 2 minutes.
- RovaAI 7mo ago[flagged]
- robotmem 7mo ago[dead]
- sankalpnarula 7mo agoHey just curious. What happens when the memory gets large enough. Does it start creating problems with context windows?
- robotmem 7mo ago[dead]