MEMBOT: Modular Memory-Based Robotic Control

• MEMBOT is a modular memory-based architecture that integrates temporal belief states to tackle intermittent sensor dropout in robotic control environments.
• It decouples belief inference from policy learning using distinct modules like an observation encoder, memory observer, and policy network, enhancing robustness and transferability.
• Empirical results demonstrate that under 50% observation availability, MEMBOT retains up to 80% of peak performance, outperforming memoryless architectures.

MEMBOT is a modular memory-based architecture developed to address the challenge of intermittent partial observability in robotic control under real-world constraints. In practice, robotic systems frequently operate in conditions where observations are noisy, occluded, or entirely unavailable due to sensor failures, network interruptions, or environmental limitations. Typical reinforcement learning (RL) approaches, which assume full observability of the system state, prove inadequate in these settings. MEMBOT advances the state of the art by decoupling belief inference from policy learning, enabling robust, transferable, and data-efficient policy deployment even when significant portions of observation sequences are missing.

1. System Architecture

MEMBOT comprises three primary components: the observation encoder, the memory-based belief observer, and the task-specific policy network. The system formalizes belief state inference and action selection as follows:

• Observation Encoder $f_{(\phi)}$: Processes raw sensory inputs $\tilde{o}_t$ (which may be masked or noisy), producing latent representations $e_t = f_{(\phi)}(\tilde{o}_t)$.
• Memory-Based Observer $g_{(\psi)}$: Aggregates encoded observations over time to maintain a latent belief state $b_t$ and hidden state $h_t$. Two implementations are supported: state-space models (SSM) and long short-term memory networks (LSTM).
• Policy Network $\pi_{(\theta)}$: Consumes the belief $b_t$ to generate an action distribution for the robot.

The overall update is given by: $$F_{(\phi,\psi)}(h_{t-1}, f_{(\phi)}(\tilde{o}_t)) = g_{(\psi)}(f_{(\phi)}(\tilde{o}_t), h_{t-1})$$ where $\tilde{o}_t = m_t \cdot o_t$, with $m_t \in \{0, 1\}$ indicating the availability of the observation at time $t$.

Both SSM and LSTM observers allow belief states to persist and integrate temporal context, even under prolonged observation dropout.

2. Belief Encoder Design

The belief encoder underlies MEMBOT’s capability to infer and retain latent state representations in partially observable Markov decision processes (POMDP), including those with intermittent observability (“intermittent POMDP”). Two designs are offered:

• State-Space Model (SSM): Implements a linear temporal update

$h_t = \tanh(W_h h_{t-1} + W_o o_t + b)$

This approach is lightweight and suitable for streaming scenarios.

• LSTM-Based Observer: Employs recurrent cell and hidden state updates, capturing longer or nonlinear dependencies. Both designs are trained to integrate past observations and actions, smoothing temporally sparse inputs into rich belief representations.

The decoupling of belief estimation enables application to domains with arbitrary observation patterns and supports transfer learning across tasks.

3. Training Protocol

MEMBOT employs a two-phase training protocol to decouple robust state estimation from task-specific policy learning:

A. Multi-Task Offline Pretraining:

Expert demonstration datasets from multiple manipulation tasks (e.g., MetaWorld, Robomimic) are used to learn a general belief encoder. The objective combines:

• Behavior Cloning Loss:

$$L_{BC} = -\mathbb{E}_{b_t, a^*_t}[\log \pi_{(\theta)}(a^*_t | b_t)]$$

where $a^*_t$ are expert actions.

• Reconstruction Loss:

$$L_{recon} = -\mathbb{E}_{b_t, o_t}[\log p_{(\xi)}(o_t | b_t)]$$

The decoder $p_{(\xi)}$ enforces that belief states contain comprehensive environmental information.

Total loss: $L_{pretrain} = L_{BC} + \lambda \cdot L_{recon}$ where $\lambda$ weights reconstruction.

Observation dropout during pretraining is simulated with random masking at probability $p_{mask}$.

B. Task-Specific Fine-Tuning:

After pretraining, the encoder is fine-tuned per task using Soft Actor-Critic (SAC). Critic networks are updated with: $$\hat Q_t = r_t + \gamma \min_k Q_{(\psi_k)}(b_{t+1}, a_{t+1})$$ while the actor loss includes entropy regularization: $$L_{actor} = -\mathbb{E}_{b_t, a_t}\left[ \frac{1}{N} \sum_{k=1}^N Q_{(\psi_k)}(b_t, a_t) - \log \pi_{(\theta)}(a_t|b_t) \right]$$ This two-phase protocol enables initial learning of robust, task-agnostic beliefs and subsequent task adaptation without retraining from scratch.

4. Empirical Performance

MEMBOT was evaluated across 10 benchmark tasks (MetaWorld, Robomimic) under varying observation availability. Key findings:

• Resilience to Dropout: Under 50% observation availability, MEMBOT retains up to 80% of peak performance, whereas memoryless baselines drop to 10–30% success rates.
• Superior Sample Efficiency: Incorporation of temporal memory and reconstruction losses leads to more rapid policy adaptation and improved sample efficiency compared to naive recurrent or memoryless architectures.
• Architecture Variations: Both SSM and LSTM observers outperform counterparts that lack explicit memory. Under full observability, differences diminish—suggesting memory mechanisms are especially impactful where observability is degraded.

Ablation studies demonstrate the necessity of decoupled belief modeling and joint optimization for robustness.

5. Deployment and Transferability

MEMBOT’s architecture is suited for real-world robotics in intermittent and partially observable settings, including:

• Autonomous Manipulation: Robots operating in cluttered or occluded spaces benefit from persistent belief encoders.
• Service Robotics and Automation: Sensor failures can be tolerated without catastrophic policy degradation.
• Transfer Learning: The belief encoder, pretrained across multiple tasks, allows for efficient policy fine-tuning when new tasks are added. Only the policy head requires adaptation, reducing data and time requirements.

Explicit modeling of latent beliefs fosters robustness and facilitates transfer across diverse robotic environments.

6. Limitations and Prospective Directions

Several challenges remain:

• Scalability: Fixed-length LSTM/SSM architectures may restrict performance in long-horizon or highly dynamic tasks. Proposed future work includes integrating transformer-based or selective state-space models, such as Mamba, to address extended temporal dependencies.
• Training Complexity: Limited fine-tuning steps (~100,000) and input dimensionality necessitate further evaluation over longer horizons and richer sensor modalities (e.g., visual data).
• Ethical Considerations: The reliance on latent belief states for decision-making mandates careful attention to transparency and accountability in safety-critical deployments.
• Generalization: Additional research is required to characterize belief encoder generalization when observability conditions change unexpectedly.

Continuing exploration includes ablation and sensitivity analyses, improved architectural scalability, and integration of expressive memory encoders.

7. Significance and Impact

MEMBOT advances the field of POMDP robotic control by explicitly modeling temporally persistent latent beliefs, separating inference from policy optimization. This facilitates resilient, transferable, and data-efficient deployment in settings where traditional memoryless RL degrades. The results demonstrate sustained performance under severe observation dropout and open new directions for scalable, memory-based RL in real-world, dynamic environments (Liang et al., 14 Sep 2025).