Human-Aligned Decision Transformers for planetary geology survey missions for low-power autonomous deployments

Introduction: A Lesson from the Desert

My journey into human-aligned AI for extreme environments began not in a clean lab, but in the dusty, sun-baked expanse of the Arizona desert. I was part of a field test for a prototype rover, a small, solar-powered machine tasked with autonomously mapping a simulated Martian terrain. The goal was simple: identify and classify geological features of interest. The reality was a masterclass in frustration. The rover, running a sophisticated but rigid reinforcement learning policy, would often get "stuck" in a loop—repeatedly scanning the same unremarkable patch of basalt while ignoring a fascinating sedimentary outcrop just meters away. It was optimizing for "coverage" and "energy efficiency," metrics we had programmed, but it was missing the point of the mission. It wasn't thinking like a geologist.

This experience was a profound learning moment. While exploring the intersection of offline reinforcement learning and transformer architectures, I discovered a critical gap: we can train agents to achieve high scores on benchmarks, but aligning their intrinsic decision-making process with high-level, often implicit, human scientific goals is a different challenge entirely. The rover wasn't wrong; it was misaligned. This realization sparked my deep dive into Human-Aligned Decision Transformers, a paradigm I believe is essential for the next generation of autonomous systems, particularly for constrained, high-stakes environments like planetary survey missions.

Technical Background: From Sequence Modeling to Aligned Autonomy

Decision Transformers (DT) revolutionized how we think about sequential decision-making. Framing reinforcement learning as a sequence modeling problem, they treat trajectories as sequences of states, actions, and returns-to-go (RTG), and use a causal transformer to predict actions autoregressively. The beauty is its simplicity and its leverage of the transformer's powerful pattern recognition.

The Core DT Formulation:
A trajectory is represented as:
τ = (R_0, s_0, a_0, R_1, s_1, a_1, ..., R_T, s_T, a_T)
Where R_t is the return-to-go from that timestep. The model is trained to predict the action a_t given the previous sequence and the desired RTG.

However, during my investigation of standard DT implementations, I found that the RTG is a blunt instrument. It encodes a scalar reward target, but planetary geology isn't about maximizing a single number. It's a multi-objective, curiosity-driven, and adaptive process. A scientist wants to:

1. Classify rock types.
2. Discover anomalies and novel formations.
3. Contextualize findings within the broader terrain.
4. Conserve precious energy and communication bandwidth.
5. Adapt the survey plan based on new discoveries.

Standard DTs, trained on offline datasets of past trajectories, learn to mimic the behavior in the data, not necessarily the underlying intent or scientific value system. This is the alignment problem.

Human Alignment through Preference Modeling and Latent Goals

My research led me to techniques from large language model alignment, particularly Direct Preference Optimization (DPO) and Reinforcement Learning from Human Feedback (RLHF). The key insight is to not just mimic actions, but to learn a reward function or a preference model that reflects human judgment. For a rover, this isn't about "liking" one image over another, but about a geologist ranking trajectories based on their perceived scientific yield.

One approach I experimented with involves a two-stage process:

1. Trajectory Ranking Dataset Creation: Using a simulator (like NASA's ROAMS or even high-fidelity game engines), I generated thousands of candidate survey trajectories. A simple script, acting as a "proxy geologist," scored them based on multi-objective criteria (diversity of samples, proximity to features, energy use). This created a dataset of trajectory pairs where one was preferred over the other.
2. Training a Preference Model: A transformer-based model learns to predict which of two trajectory segments a human (or proxy) would prefer.

import torch
import torch.nn as nn

class TrajectoryPreferenceModel(nn.Module):
    """A model to score the human-aligned scientific value of a trajectory segment."""
    def __init__(self, state_dim, act_dim, hidden_dim=256):
        super().__init__()
        # Embeddings for state, action, and a special [CLS] token
        self.state_embed = nn.Linear(state_dim, hidden_dim)
        self.act_embed = nn.Linear(act_dim, hidden_dim)
        self.pos_embed = nn.Embedding(1000, hidden_dim) # positional encoding

        encoder_layer = nn.TransformerEncoderLayer(d_model=hidden_dim, nhead=8, batch_first=True)
        self.transformer = nn.TransformerEncoder(encoder_layer, num_layers=3)

        # CLS token for final trajectory representation
        self.cls_token = nn.Parameter(torch.randn(1, 1, hidden_dim))
        self.value_head = nn.Sequential(
            nn.Linear(hidden_dim, 128),
            nn.ReLU(),
            nn.Linear(128, 1)  # Scalar preference score
        )

    def forward(self, states, actions):
        # states: (batch, seq_len, state_dim)
        # actions: (batch, seq_len, act_dim)
        batch_size, seq_len = states.shape[0], states.shape[1]

        state_emb = self.state_embed(states)
        act_emb = self.act_embed(actions)
        token_emb = state_emb + act_emb  # Combine state/action info

        # Add positional encoding
        positions = torch.arange(seq_len, device=states.device).unsqueeze(0).expand(batch_size, -1)
        pos_emb = self.pos_embed(positions)
        token_emb = token_emb + pos_emb

        # Prepend CLS token
        cls_tokens = self.cls_token.expand(batch_size, -1, -1)
        token_emb = torch.cat([cls_tokens, token_emb], dim=1)

        # Transformer processing
        transformed = self.transformer(token_emb)
        cls_output = transformed[:, 0, :]  # Take CLS token output
        value = self.value_head(cls_output)
        return value  # (batch, 1)

# Example of preference loss (simplified Bradley-Terry model)
def preference_loss(pref_model, traj_A, traj_B, labels):
    """labels=1 if traj_A preferred, 0 if traj_B preferred."""
    score_A = pref_model(traj_A['states'], traj_A['actions'])
    score_B = pref_model(traj_B['states'], traj_B['actions'])

    logits = score_A - score_B
    loss = nn.functional.binary_cross_entropy_with_logits(logits, labels.float())
    return loss

This preference model can then be used to relabel or score trajectories in the offline dataset, providing a dense, human-aligned "reward" signal that the Decision Transformer can learn from, replacing or augmenting the simplistic RTG.

Implementation Details: The Aligned Decision Transformer for Low-Power Deployment

The real challenge emerges when we need this sophisticated model to run on a rover's onboard computer, which is severely constrained by power, thermal limits, and compute resources (think ARM Cortex-A series or radiation-hardened FPGAs). My experimentation focused on three key pillars: architecture modification, knowledge distillation, and adaptive inference.

1. Architecture Modifications for Efficiency

Standard transformers are parameter-heavy. For low-power deployment, I explored several modifications based on recent literature and my own benchmarks:

• State-Space Layers (S4/Mamba): While exploring alternative sequence models, I realized that structured state-space sequence models (S4) and their selective variants (Mamba) offer near-constant memory use and linear-time scaling with sequence length, which is perfect for long-duration rover trajectories. I prototyped a hybrid model where the backbone was a Mamba block, not a transformer.
• Grouped Query Attention (GQA): If sticking with transformers, GQA significantly reduces the memory footprint of the key-value cache during autoregressive action prediction, a critical factor for deployment.
• Binary/Ternary Weight Quantization: Post-training quantization (PTQ) to 8-bit integers is standard. For extreme savings, I experimented with training-aware quantization, pushing weights to 2 or 3 bits. The accuracy drop for control tasks was less severe than I initially feared, especially when combined with knowledge distillation.

# Simplified example of a Mamba-based decision block (conceptual)
# Note: Using a simplified SSM for illustration. Real Mamba is more complex.
import torch
import torch.nn as nn

class SimplifiedSSMDecisionBlock(nn.Module):
    """A simplified state-space block for sequential decision prediction."""
    def __init__(self, d_model, d_state=64, dt_rank=16):
        super().__init__()
        self.in_proj = nn.Linear(d_model, d_model * 2)
        self.dt_proj = nn.Linear(dt_rank, d_model)

        # State parameters (simplified)
        self.A = nn.Parameter(torch.randn(d_model, d_state))
        self.B_proj = nn.Linear(d_model, d_state)
        self.C_proj = nn.Linear(d_model, d_state)

        self.out_proj = nn.Linear(d_model, d_model)

    def forward(self, x):
        # x: (batch, seq_len, d_model)
        u, v = self.in_proj(x).chunk(2, dim=-1)
        u = torch.tanh(u)  # 'Input-dependent' processing

        # Very simplified discretization & scan (conceptual)
        # In real Mamba, this is a highly optimized selective scan.
        batch, seq_len, d_model = u.shape
        states = torch.zeros(batch, d_model, self.A.shape[-1], device=x.device)
        outputs = []

        for t in range(seq_len):
            B_t = self.B_proj(u[:, t, :])  # Input-dependent B
            C_t = self.C_proj(u[:, t, :])  # Input-dependent C
            # Discrete state update (simplified)
            states = torch.matmul(states, self.A.t()) + B_t.unsqueeze(1)
            y_t = (states * C_t.unsqueeze(1)).sum(dim=-1)
            outputs.append(y_t)

        y = torch.stack(outputs, dim=1)
        y = y * torch.sigmoid(v)  # Gating mechanism
        return self.out_proj(y)

2. Knowledge Distillation: From Teacher to Student

The full-sized, human-aligned DT (the "teacher") is too large for deployment. My approach was to distill its knowledge into a much smaller "student" network. Crucially, I distilled not just the action predictions, but also the trajectory preferences.

def distillation_loss(student, teacher, states, actions, target_rtg):
    """
    Distill knowledge from a large teacher DT to a small student DT.
    """
    # Action prediction loss (standard)
    student_actions = student(states, actions, target_rtg)
    teacher_actions = teacher(states, actions, target_rtg).detach()
    action_loss = nn.functional.mse_loss(student_actions, teacher_actions)

    # Hidden state similarity loss (forces similar representations)
    # Assume we have access to a middle layer's output
    student_hidden = student.get_hidden(states, actions, target_rtg)
    teacher_hidden = teacher.get_hidden(states, actions, target_rtg).detach()
    hidden_loss = nn.functional.cosine_embedding_loss(
        student_hidden, teacher_hidden,
        torch.ones(student_hidden.size(0), device=student_hidden.device)
    )

    # Preference consistency loss (most important for alignment)
    # Generate short trajectory rollouts from student and teacher
    student_traj = rollout(student, states[:, 0, :], target_rtg[:, 0])
    teacher_traj = rollout(teacher, states[:, 0, :], target_rtg[:, 0])

    # Use the frozen preference model to score both
    with torch.no_grad():
        pref_model.eval()
        student_score = pref_model(student_traj['states'], student_traj['actions'])
        teacher_score = pref_model(teacher_traj['states'], teacher_traj['actions'])

    pref_loss = nn.functional.mse_loss(student_score, teacher_score)

    total_loss = action_loss + 0.5 * hidden_loss + 2.0 * pref_loss
    return total_loss

3. Adaptive Inference and Mixture of Experts (MoE)

A rover's operational context changes: cruising on flat terrain, carefully approaching a target, or performing an intensive in-situ measurement. Through studying dynamic neural networks, I learned that we don't need the full model complexity all the time. I implemented a sparse Mixture of Experts (MoE) system within the DT architecture. A lightweight router network selects one of several small "expert" networks (e.g., "Navigation Expert," "Sampling Expert," "Anomaly Investigation Expert") for each segment of the trajectory. This drastically reduces active parameters during any single inference step.

Real-World Application: The Planetary Geology Survey Loop

Let's synthesize this into a concrete deployment pipeline for our low-power rover:

1. Earth-Based Training: The human-aligned DT (teacher) is trained on massive, simulated datasets scored by geologist proxies (and eventually real human rankings). The preference model is baked into its understanding of RTG.
2. Distillation & Quantization: The teacher is distilled into a tiny, quantized student model (e.g., <10M parameters, INT4 precision). This model is verified and uploaded.
3. Onboard Execution: The rover runs the student model. The input sequence is a compact representation of recent sensor observations (lidar scans, multispectral images downsampled via a tiny CNN), past actions, and a dynamic RTG. This RTG is not a fixed target, but is continuously adjusted by a lightweight "science value estimator" (a micro-version of the preference model) that looks at recent discoveries and remaining energy.
4. Adaptive Planning: The MoE router dynamically chooses which expert to engage. While traversing to a pre-identified target, it uses the Navigation Expert. Upon detecting a spectral anomaly, it switches to the Anomaly Investigation Expert, which might decide to take a close-up image or even adjust the path for a quick contact measurement.
5. Data Prioritization & Communication: The rover uses its own internal preference score to tag data packets. High-value data (e.g., "unusual mineral signature") is given priority for limited uplink bandwidth.

Challenges and Solutions from the Trenches

Challenge 1: The Sim-to-Real Gap for "Science Value."
The proxy geologist script in simulation is a poor substitute for human intuition. My solution was to incorporate a passive learning loop. When the rover is in communication, it can send trajectory summaries and receive sparse human feedback ("Why did you ignore that feature?" "Good job sampling that layer."). This feedback is used to perform a lightweight fine-tuning of the preference model, even on the edge device using algorithms like Elastic Weight Consolidation to avoid catastrophic forgetting.

Challenge 2: Catastrophic Forgetting in Dynamic Environments.
A model distilled for Mars-like terrain might fail on icy Europa. Through my exploration of continual learning, I implemented replay buffers on the edge. The rover stores a small, high-priority set of its own experienced trajectories (especially surprising or high-value ones). During idle periods, it performs micro-fine-tuning sessions on this buffer, ensuring it adapts to the real environment without forgetting its core knowledge.

Challenge 3: Verifying Alignment is Hard.
How do we know the rover is truly "aligned" and not just finding a clever hack to maximize its proxy score? This is an open research problem. My practical approach involved rigorous scenario testing in simulation, including adversarial scenarios where the "correct" action requires sacrificing short-term score for long-term science. I also worked on generating interpretable explanations from the model's latent space—e.g., "I am approaching this rock because my spectral expert pathway activated strongly for hydrated minerals."

Future Directions: Quantum-Inspired Optimization and Neuromorphic Hardware

Looking ahead, two areas are particularly promising. First, quantum-inspired optimization algorithms (like Quantum Approximate Optimization Algorithm - QAOA simulated on classical hardware) could solve the complex, multi-objective planning problem inherent in survey missions more efficiently than gradient-based methods, especially for global path re-planning.

Second, the ultimate low-power deployment may be on neuromorphic processors like Intel's Loihi. These chips mimic the brain's spiking neurons and are incredibly energy-efficient for temporal processing. My initial experiments involved converting the distilled DT into a Spiking Neural Network (SNN). The event-driven nature of SNNs is a natural fit for processing asynchronous sensor data (a new image arrives, a vibration is detected) which could further slash power consumption.

Conclusion: Building Partners for Discovery

The desert rover that got stuck taught me that autonomy is not just about independence; it's about partnership. The goal of Human-Aligned Decision Transformers is not to replace the scientist on Earth, but to create a surrogate in the field—an agent that internalizes the scientist's values, curiosity, and methodological rigor. It must be efficient enough to run on a trickle of solar power, robust enough to survive cosmic rays, and wise enough to know when a dull-looking rock is actually a clue to an ancient riverbed.

My learning journey from that dusty field test to implementing sparse transformers and preference models has convinced me that this alignment challenge is the central problem for trustworthy AI in exploration. By baking human scientific values directly into the decision-making fabric of these autonomous agents, we move from building tools that execute commands to creating partners that can truly share in the