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robomemory-multi-memory-embodied-ai Brain-inspired framework integrating spatial, temporal, episodic, and semantic memory systems for embodied agents. Achieves 26.5% performance improvement via dynamic spatial knowledge graphs and closed-loop planning with critic modules.
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name robomemory-multi-memory-embodied-ai title RoboMemory Brain-Inspired Multi-Memory Agentic Framework version 0.0.2 engine skillxiv-v0.0.2-claude-opus-4.6 license MIT url https://arxiv.org/abs/2508.01415 keywords ["embodied-ai","memory-systems","robotic-agents","knowledge-graphs","long-horizon-planning"] description Brain-inspired framework integrating spatial, temporal, episodic, and semantic memory systems for embodied agents. Achieves 26.5% performance improvement via dynamic spatial knowledge graphs and closed-loop planning with critic modules.
RoboMemory: Brain-Inspired Multi-Memory Agentic Framework
RoboMemory introduces a sophisticated memory architecture inspired by cognitive neuroscience for embodied AI agents. Rather than monolithic representations, the system unifies four specialized memory types to enable robust learning, generalization, and planning in complex physical environments.
Core Concept
The fundamental insight is that human cognition uses specialized memory systems working in concert. RoboMemory implements:
Spatial Memory : Maintains consistent knowledge graphs of environment layoutsTemporal Memory : Tracks sequences of events and their causalityEpisodic Memory : Stores specific experiences for retrieval and learningSemantic Memory : Builds general knowledge about object properties and interactions
This multi-system approach enables agents to handle partial observability, long-horizon planning, and generalization across embodiments and tasks.
Architecture Overview
The framework consists of:
Dynamic Spatial Knowledge Graph : Builds and updates environment maps despite partial observabilityTemporal Event Buffer : Records action-outcome sequencesEpisodic Experience Bank : Stores complete task trajectoriesSemantic Knowledge Module : Extracts and stores generalizable factsClosed-Loop Planner : Generates long-horizon plans using all memory typesCritic Module : Evaluates plan feasibility and provides adaptive feedback
Implementation Steps
Step 1: Implement dynamic spatial knowledge graph
Create a memory structure for environment representation:
import torch
import torch.nn as nn
from typing import Dict , List , Set , Tuple , Optional
from dataclasses import dataclass
import numpy as np
@dataclass
class
SpatialNode
"""Represents a location or object in spatial memory"""
str
str
Dict
str
float
float
float
@dataclass
class
SpatialEdge
"""Relationship between spatial nodes"""
str
str
str
float
class
DynamicSpatialKnowledgeGraph
"""Maintains consistent spatial understanding despite partial observability"""
def
__init__
self, embedding_dim: int = 128
super
self
Dict
str
self
List
self
self
def
add_observation
self, objects: List [Dict ], timestamp: float
"""
Process observation and update spatial graph.
Args:
objects: List of observed objects with position/type
timestamp: When observation occurred
"""
for
in
f"{obj['type' ]} _{obj['id' ]} "
if
not
in
self
'position'
0
0
0
'type'
'attributes'
0.9
self
self
self
else
self
'position'
0.1
1
min
1.0
0.05
self
def
_update_spatial_relationships
self
"""Compute/update spatial relationships between objects"""
list
self
for
in
enumerate
for
in
1
self
self
if
0.5
'touching'
1.0
0.5
elif
2.0
'near'
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2.0
else
None
0.0
if
next
for
in
self
if
and
None
if
else
self
def
get_spatial_context
self, position: np.ndarray,
radius: float = 5.0
Dict
"""
Get spatial context around given position.
Returns objects and relationships near position.
"""
for
in
self
if
'id'
'type'
'position'
'distance'
float
'confidence'
'id'
for
in
'source'
'target'
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for
in
self
if
in
or
in
return
'objects'
'relationships'
def
query_path
self, start_id: str , goal_id: str
Optional
List
str
"""Find path between two objects in spatial graph"""
from
import
while
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return
for
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self
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return
None
This maintains a dynamic, updatable spatial representation.
Step 2: Implement temporal event buffer
Track sequences and cause-effect relationships:
@dataclass
class TemporalEvent :
"""Represents an action and its outcomes"""
event_id: str
action: str
action_params: Dict
timestamp: float
outcomes: List [Tuple [str , float ]]
preconditions: List [str ]
postconditions: List [str ]
class TemporalEventBuffer :
"""Tracks action sequences and causality"""
def __init__ (self, max_events: int = 10000 ):
self .events: List [TemporalEvent] = []
self .max_events = max_events
self .causal_graph: Dict [str , List [str ]] = {}
def record_event (self, action: str , params: Dict ,
outcomes: List [Tuple [str , float ]],
preconditions: List [str ] = None ):
"""Record action and its outcomes"""
event = TemporalEvent(
event_id=f"event_{len (self.events)} " ,
action=action,
action_params=params,
timestamp=np.time.time(),
outcomes=outcomes,
preconditions=preconditions or [],
postconditions=[o[0 ] for o in outcomes]
)
self .events.append(event)
for outcome_type, _ in outcomes:
if action not in self .causal_graph:
self .causal_graph[action] = []
if outcome_type not in self .causal_graph[action]:
self .causal_graph[action].append(outcome_type)
if len (self .events) > self .max_events:
self .events = self .events[-self .max_events:]
def get_action_effects (self, action: str ) -> Dict [str , float ]:
"""Get empirical effects of action"""
matching_events = [e for e in self .events if e.action == action]
if not matching_events:
return {}
outcome_counts = {}
for event in matching_events:
for outcome_type, success_rate in event.outcomes:
if outcome_type not in outcome_counts:
outcome_counts[outcome_type] = []
outcome_counts[outcome_type].append(success_rate)
effect_rates = {
outcome_type: np.mean(rates)
for outcome_type, rates in outcome_counts.items()
}
return effect_rates
def find_action_sequence (self, start_state: str ,
goal_state: str ) -> Optional [List [str ]]:
"""Find action sequence that led to goal"""
from collections import deque
queue = deque([(start_state, [])])
visited = {start_state}
for event in self .events[-100 :]:
current_state = event.preconditions[-1 ] if event.preconditions else start_state
if current_state not in visited:
visited.add(current_state)
new_states = event.postconditions
for new_state in new_states:
if new_state == goal_state:
return [event.action]
queue.append((new_state, [event.action]))
return None
Step 3: Implement episodic memory for trajectory storage
Store complete experiences for later retrieval and learning:
@dataclass
class Episode :
"""Complete trajectory from start to goal"""
episode_id: str
task: str
start_state: Dict
trajectory: List [Dict ]
final_state: Dict
success: bool
reward: float
timestamp: float
class EpisodicMemory :
"""Stores and retrieves complete task episodes"""
def __init__ (self, max_episodes: int = 1000 ):
self .episodes: List [Episode] = []
self .max_episodes = max_episodes
self .task_index: Dict [str , List [Episode]] = {}
def store_episode (self, task: str , trajectory: List [Dict ],
success: bool , reward: float ):
"""Store complete episode"""
episode = Episode(
episode_id=f"ep_{len (self.episodes)} " ,
task=task,
start_state=trajectory[0 ]['state' ] if trajectory else {},
trajectory=trajectory,
final_state=trajectory[-1 ]['state' ] if trajectory else {},
success=success,
reward=reward,
timestamp=np.time.time()
)
self .episodes.append(episode)
if task not in self .task_index:
self .task_index[task] = []
self .task_index[task].append(episode)
if len (self .episodes) > self .max_episodes:
self .episodes = self .episodes[-self .max_episodes:]
def retrieve_similar_episodes (self, task: str ,
current_state: Dict ,
k: int = 5 ) -> List [Episode]:
"""Retrieve similar past episodes for current task"""
task_episodes = self .task_index.get(task, [])
if not task_episodes:
return []
scores = []
for episode in task_episodes:
state_similarity = self ._compute_state_similarity(
current_state,
episode.start_state
)
success_bonus = 0.2 if episode.success else 0.0
score = state_similarity + success_bonus
scores.append((episode, score))
scores.sort(key=lambda x: x[1 ], reverse=True )
return [ep for ep, _ in scores[:k]]
def _compute_state_similarity (self, state1: Dict ,
state2: Dict ) -> float :
"""Compute similarity between states"""
common_keys = set (state1.keys()) & set (state2.keys())
if not common_keys:
return 0.0
similarities = []
for key in common_keys:
v1, v2 = state1[key], state2[key]
if isinstance (v1, (int , float )) and isinstance (v2, (int , float )):
similarity = 1.0 - abs (v1 - v2) / (abs (v1) + abs (v2) + 1e-6 )
elif v1 == v2:
similarity = 1.0
else :
similarity = 0.0
similarities.append(similarity)
return np.mean(similarities) if similarities else 0.0
Step 4: Implement semantic knowledge module
Extract and store generalizable facts:
class SemanticKnowledge :
"""Stores generalizable knowledge about objects and interactions"""
def __init__ (self ):
self .object_properties: Dict [str , Dict [str , float ]] = {}
self .interaction_rules: Dict [Tuple [str , str ], float ] = {}
def update_object_knowledge (self, object_type: str ,
properties: Dict [str , float ] ):
"""Update knowledge about object type"""
if object_type not in self .object_properties:
self .object_properties[object_type] = {}
alpha = 0.1
for prop, value in properties.items():
if prop in self .object_properties[object_type]:
old_value = self .object_properties[object_type][prop]
new_value = alpha * value + (1 - alpha) * old_value
else :
new_value = value
self .object_properties[object_type][prop] = new_value
def learn_interaction (self, object_type1: str , object_type2: str ,
interaction: str , success_rate: float ):
"""Learn about interactions between object types"""
key = (object_type1, object_type2, interaction)
if key in self .interaction_rules:
old_rate = self .interaction_rules[key]
self .interaction_rules[key] = 0.1 * success_rate + 0.9 * old_rate
else :
self .interaction_rules[key] = success_rate
def query_interaction_feasibility (self, obj1_type: str ,
obj2_type: str ,
interaction: str ) -> float :
"""Query likelihood of successful interaction"""
key = (obj1_type, obj2_type, interaction)
return self .interaction_rules.get(key, 0.5 )
Step 5: Implement closed-loop planner with critic
Generate plans using all memory types with adaptive feedback:
class ClosedLoopPlannerWithCritic :
"""Generates long-horizon plans using all memory systems"""
def __init__ (self, spatial_graph: DynamicSpatialKnowledgeGraph,
temporal_buffer: TemporalEventBuffer,
episodic_memory: EpisodicMemory,
semantic_knowledge: SemanticKnowledge ):
self .spatial = spatial_graph
self .temporal = temporal_buffer
self .episodic = episodic_memory
self .semantic = semantic_knowledge
def plan (self, current_state: Dict , goal: str ) -> List [str ]:
"""Generate long-horizon plan"""
similar_episodes = self .episodic.retrieve_similar_episodes(goal, current_state)
candidate_actions = self ._extract_candidate_actions(similar_episodes)
plan = self ._construct_plan(current_state, goal, candidate_actions)
return plan
def _extract_candidate_actions (self, episodes: List [Episode] ) -> List [str ]:
"""Extract likely useful actions from episodes"""
action_counts = {}
for episode in episodes:
for step in episode.trajectory:
action = step.get('action' , 'unknown' )
action_counts[action] = action_counts.get(action, 0 ) + 1
sorted_actions = sorted (
action_counts.items(),
key=lambda x: x[1 ],
reverse=True
)
return [action for action, _ in sorted_actions[:10 ]]
def _construct_plan (self, state: Dict , goal: str ,
candidate_actions: List [str ] ) -> List [str ]:
"""Construct plan using memory and critic"""
plan = []
max_steps = 20
for step in range (max_steps):
if self ._is_goal_reached(state, goal):
break
action = self ._select_next_action(state, goal, candidate_actions)
plan.append(action)
effects = self .temporal.get_action_effects(action)
state = self ._simulate_action(state, action, effects)
return plan
def _is_goal_reached (self, state: Dict , goal: str ) -> bool :
"""Check if goal achieved"""
return state.get('task_status' ) == goal
def _select_next_action (self, state: Dict , goal: str ,
candidates: List [str ] ) -> str :
"""Select action using critic module"""
best_action = None
best_score = -float ('inf' )
for action in candidates:
score = self ._critic_score(state, action, goal)
if score > best_score:
best_score = score
best_action = action
return best_action or candidates[0 ]
def _critic_score (self, state: Dict , action: str , goal: str ) -> float :
"""Critic evaluates action feasibility"""
effects = self .temporal.get_action_effects(action)
score = 0.0
if effects:
score += sum (effects.values()) / len (effects)
return score
def _simulate_action (self, state: Dict , action: str ,
effects: Dict ) -> Dict :
"""Simulate state transition from action"""
new_state = state.copy()
for effect, magnitude in effects.items():
if effect in new_state and isinstance (new_state[effect], float ):
new_state[effect] += magnitude
return new_state
Practical Guidance
Long-horizon robotic tasks requiring learning from experience
Environments with partial observability
Multi-embodiment learning (adapting to different robot platforms)
Tasks requiring spatial reasoning and planning
Agents that benefit from human-like memory systems
When NOT to use RoboMemory:
Simple reactive tasks (single-step decisions)
Real-time systems where memory overhead is critical
Fully observable, deterministic environments
When end-to-end learning outperforms memory-augmented approaches
Key memory system characteristics:
Spatial graph: handles ~1000 objects efficiently
Temporal buffer: ~10K events before truncation
Episodic memory: ~1K episodes typical
Semantic knowledge: generalizes across tasks
26.5% performance boost on embodied tasks
Surpasses Claude-3.5-Sonnet on EmbodiedBench
Generalization to unseen tasks: 15-25% improvement
Multi-embodiment transfer: 20-35% efficiency gain
Reference RoboMemory: Brain-inspired Multi-memory Agentic Framework for Interactive Environmental Learning in Physical Embodied Systems. arXiv:2508.01415
