{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:JW23WX3TYXCQD23OSGGQONQUOH","short_pith_number":"pith:JW23WX3T","schema_version":"1.0","canonical_sha256":"4db5bb5f73c5c501eb6e918d07361471c04d7b9834a137d483d70b9462cf35c4","source":{"kind":"arxiv","id":"2405.07503","version":2},"attestation_state":"computed","paper":{"title":"Consistency Policy: Accelerated Visuomotor Policies via Consistency Distillation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.RO","authors_text":"Aaditya Prasad, Jeannette Bohg, Jimmy Wu, Kevin Lin, Linqi Zhou","submitted_at":"2024-05-13T06:53:42Z","abstract_excerpt":"Many robotic systems, such as mobile manipulators or quadrotors, cannot be equipped with high-end GPUs due to space, weight, and power constraints. These constraints prevent these systems from leveraging recent developments in visuomotor policy architectures that require high-end GPUs to achieve fast policy inference. In this paper, we propose Consistency Policy, a faster and similarly powerful alternative to Diffusion Policy for learning visuomotor robot control. By virtue of its fast inference speed, Consistency Policy can enable low latency decision making in resource-constrained robotic se"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2405.07503","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2024-05-13T06:53:42Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"ab9f5e2f37bc46a22e577b7da4cf7c6e86d00b2447237db496609fc1e7605a7e","abstract_canon_sha256":"fb0bb912427d48c696163ced5ba6d3c2000f48d179462f5fb2b5e3caf4c4918b"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:38:09.670268Z","signature_b64":"yi75eHlvf7BnAGyNfbIwvKC3gTlZwu0cEe5Iy/qNbeiAkA90XEQaX6wBXz16ZlWgm1CTmaE8MWOGj/e4A6uTAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"4db5bb5f73c5c501eb6e918d07361471c04d7b9834a137d483d70b9462cf35c4","last_reissued_at":"2026-07-05T08:38:09.669806Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:38:09.669806Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Consistency Policy: Accelerated Visuomotor Policies via Consistency Distillation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.RO","authors_text":"Aaditya Prasad, Jeannette Bohg, Jimmy Wu, Kevin Lin, Linqi Zhou","submitted_at":"2024-05-13T06:53:42Z","abstract_excerpt":"Many robotic systems, such as mobile manipulators or quadrotors, cannot be equipped with high-end GPUs due to space, weight, and power constraints. These constraints prevent these systems from leveraging recent developments in visuomotor policy architectures that require high-end GPUs to achieve fast policy inference. In this paper, we propose Consistency Policy, a faster and similarly powerful alternative to Diffusion Policy for learning visuomotor robot control. By virtue of its fast inference speed, Consistency Policy can enable low latency decision making in resource-constrained robotic se"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2405.07503","kind":"arxiv","version":2},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2405.07503/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"2405.07503","created_at":"2026-07-05T08:38:09.669860+00:00"},{"alias_kind":"arxiv_version","alias_value":"2405.07503v2","created_at":"2026-07-05T08:38:09.669860+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2405.07503","created_at":"2026-07-05T08:38:09.669860+00:00"},{"alias_kind":"pith_short_12","alias_value":"JW23WX3TYXCQ","created_at":"2026-07-05T08:38:09.669860+00:00"},{"alias_kind":"pith_short_16","alias_value":"JW23WX3TYXCQD23O","created_at":"2026-07-05T08:38:09.669860+00:00"},{"alias_kind":"pith_short_8","alias_value":"JW23WX3T","created_at":"2026-07-05T08:38:09.669860+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":36,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.22480","citing_title":"ARP: Enhancing Quantized Skill Abstractions via Visual Alignment and Iterative Refinement for Robotic Manipulation","ref_index":19,"is_internal_anchor":false},{"citing_arxiv_id":"2606.21935","citing_title":"CoRDE: Concept-Prior Routed Diffusion Experts for Structural Generalization in Robot Manipulation","ref_index":9,"is_internal_anchor":false},{"citing_arxiv_id":"2606.20048","citing_title":"MirrorDuo: Reflection-Consistent Visuomotor Learning from Mirrored Demonstration Pairs","ref_index":15,"is_internal_anchor":false},{"citing_arxiv_id":"2606.11569","citing_title":"ConsistencyPlanner: Real-time Planning with Fast-Sampling Consistency Models","ref_index":17,"is_internal_anchor":false},{"citing_arxiv_id":"2606.10371","citing_title":"Test-time Adversarial Takeover: A Real-time Hijacking Interface against Robotic Diffusion Policies","ref_index":8,"is_internal_anchor":false},{"citing_arxiv_id":"2606.10040","citing_title":"Efficient-WAM: A 1B-Parameter World-Action Model with Low-Cost Future Imagination","ref_index":17,"is_internal_anchor":false},{"citing_arxiv_id":"2606.28529","citing_title":"The Speedup Paradox: Rethinking Inference Speed-Quality Trade-off in Embodied Tasks","ref_index":31,"is_internal_anchor":false},{"citing_arxiv_id":"2606.28529","citing_title":"The Speedup Paradox: Rethinking Inference Speed-Quality Trade-off in Embodied Tasks","ref_index":31,"is_internal_anchor":false},{"citing_arxiv_id":"2605.01581","citing_title":"Hyper-DP3: Frequency-Aware Right-Sizing of 3D Diffusion Policies for Visuomotor Control","ref_index":18,"is_internal_anchor":false},{"citing_arxiv_id":"2606.30318","citing_title":"Chronos: A Physics-Informed Full-History Framework for Non-Markovian Long-Horizon Manipulation","ref_index":40,"is_internal_anchor":false},{"citing_arxiv_id":"2606.29934","citing_title":"RoamFlow: Reinforcement-Aligned One-Step Action MeanFlow Policy for Image-Goal Navigation","ref_index":8,"is_internal_anchor":false},{"citing_arxiv_id":"2605.25537","citing_title":"Action-Prior Denoising for Smooth Real-Time Chunking","ref_index":8,"is_internal_anchor":false},{"citing_arxiv_id":"2605.30226","citing_title":"BORA: Bridging Offline Reinforcement Learning and Online Residual Adaptation for Real-World Dexterous VLA Models","ref_index":7,"is_internal_anchor":false},{"citing_arxiv_id":"2606.01098","citing_title":"Implicit Drifting Policy: One-Step Action Generation via Conditional Expert Geometry","ref_index":32,"is_internal_anchor":false},{"citing_arxiv_id":"2605.14598","citing_title":"DSSP: Diffusion State Space Policy with Full-History Encoding","ref_index":43,"is_internal_anchor":false},{"citing_arxiv_id":"2605.21862","citing_title":"EvoScene-VLA: Evolving Scene Beliefs Inside the Action Decoder for Chunked Robot Control","ref_index":27,"is_internal_anchor":false},{"citing_arxiv_id":"2601.12894","citing_title":"Sparse ActionGen: Accelerating Diffusion Policy with Real-time Pruning","ref_index":11,"is_internal_anchor":false},{"citing_arxiv_id":"2605.15944","citing_title":"FocalPolicy: Frequency-Optimized Chunking and Locally Anchored Flow Matching for Coherent Visuomotor Policy","ref_index":4,"is_internal_anchor":false},{"citing_arxiv_id":"2605.15944","citing_title":"FocalPolicy: Frequency-Optimized Chunking and Locally Anchored Flow Matching for Coherent Visuomotor Policy","ref_index":4,"is_internal_anchor":false},{"citing_arxiv_id":"2510.08547","citing_title":"R2RGEN: Real-to-Real 3D Data Generation for Spatially Generalized Manipulation","ref_index":21,"is_internal_anchor":false},{"citing_arxiv_id":"2601.21926","citing_title":"Information Filtering via Variational Regularization for Robot Manipulation","ref_index":11,"is_internal_anchor":false},{"citing_arxiv_id":"2503.10631","citing_title":"HybridVLA: Collaborative Diffusion and Autoregression in a Unified Vision-Language-Action Model","ref_index":53,"is_internal_anchor":false},{"citing_arxiv_id":"2603.05117","citing_title":"SeedPolicy: Horizon Scaling via Self-Evolving Diffusion Policy for Robot Manipulation","ref_index":24,"is_internal_anchor":false},{"citing_arxiv_id":"2506.07339","citing_title":"Real-Time Execution of Action Chunking Flow Policies","ref_index":49,"is_internal_anchor":false},{"citing_arxiv_id":"2603.15757","citing_title":"You've Got a Golden Ticket: Improving Generative Robot Policies With A Single Noise Vector","ref_index":25,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/JW23WX3TYXCQD23OSGGQONQUOH","json":"https://pith.science/pith/JW23WX3TYXCQD23OSGGQONQUOH.json","graph_json":"https://pith.science/api/pith-number/JW23WX3TYXCQD23OSGGQONQUOH/graph.json","events_json":"https://pith.science/api/pith-number/JW23WX3TYXCQD23OSGGQONQUOH/events.json","paper":"https://pith.science/paper/JW23WX3T"},"agent_actions":{"view_html":"https://pith.science/pith/JW23WX3TYXCQD23OSGGQONQUOH","download_json":"https://pith.science/pith/JW23WX3TYXCQD23OSGGQONQUOH.json","view_paper":"https://pith.science/paper/JW23WX3T","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2405.07503&json=true","fetch_graph":"https://pith.science/api/pith-number/JW23WX3TYXCQD23OSGGQONQUOH/graph.json","fetch_events":"https://pith.science/api/pith-number/JW23WX3TYXCQD23OSGGQONQUOH/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/JW23WX3TYXCQD23OSGGQONQUOH/action/timestamp_anchor","attest_storage":"https://pith.science/pith/JW23WX3TYXCQD23OSGGQONQUOH/action/storage_attestation","attest_author":"https://pith.science/pith/JW23WX3TYXCQD23OSGGQONQUOH/action/author_attestation","sign_citation":"https://pith.science/pith/JW23WX3TYXCQD23OSGGQONQUOH/action/citation_signature","submit_replication":"https://pith.science/pith/JW23WX3TYXCQD23OSGGQONQUOH/action/replication_record"}},"created_at":"2026-07-05T08:38:09.669860+00:00","updated_at":"2026-07-05T08:38:09.669860+00:00"}